diff --git a/day_1/ex_01_introduction_to_pyiron.ipynb b/day_1/ex_01_introduction_to_pyiron.ipynb
index 2a00e422147f7f9cd050e2738099036bd13a8700..82abd81802eb2b18e3cb8c348890b2d27a1292a6 100644
--- a/day_1/ex_01_introduction_to_pyiron.ipynb
+++ b/day_1/ex_01_introduction_to_pyiron.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "varying-litigation",
+ "id": "sound-strength",
"metadata": {},
"source": [
"# [**Workflows for atomistic simulations**](http://potentials.rub.de/) "
@@ -10,7 +10,7 @@
},
{
"cell_type": "markdown",
- "id": "numerous-egypt",
+ "id": "immediate-edinburgh",
"metadata": {},
"source": [
"## **Day 1 - Atomistic simulations with [pyiron](https://pyiron.org)**\n",
@@ -31,7 +31,7 @@
},
{
"cell_type": "markdown",
- "id": "beneficial-element",
+ "id": "remarkable-senegal",
"metadata": {},
"source": [
"### **Importing necessary libraries**\n",
@@ -42,7 +42,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "beneficial-republic",
+ "id": "concerned-rates",
"metadata": {},
"outputs": [],
"source": [
@@ -53,7 +53,7 @@
},
{
"cell_type": "markdown",
- "id": "color-membership",
+ "id": "severe-aruba",
"metadata": {},
"source": [
"Fundamentally, we only need to import one module from `pyiron`: the `Project` class"
@@ -62,7 +62,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "religious-adapter",
+ "id": "immune-federation",
"metadata": {},
"outputs": [],
"source": [
@@ -71,7 +71,7 @@
},
{
"cell_type": "markdown",
- "id": "spectacular-shark",
+ "id": "nuclear-violence",
"metadata": {},
"source": [
"The Project object introduced below is central in pyiron. It allows to name the project as well as to derive all other objects such as structures, jobs etc. without having to import them. Thus, by code completion *Tab* the respective commands can be found easily.\n",
@@ -81,7 +81,7 @@
},
{
"cell_type": "markdown",
- "id": "studied-recruitment",
+ "id": "czech-target",
"metadata": {},
"source": [
"### **Creation of a project instance**"
@@ -90,7 +90,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "knowing-rating",
+ "id": "short-biodiversity",
"metadata": {},
"outputs": [],
"source": [
@@ -99,7 +99,7 @@
},
{
"cell_type": "markdown",
- "id": "solid-protein",
+ "id": "civilian-wireless",
"metadata": {},
"source": [
"The project name also applies for the directory that is created for the project. All data generated by this `Project` object resides in this directory."
@@ -108,13 +108,13 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "relative-belle",
+ "id": "other-parade",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'/home/surendralal/notebooks/pyiron_potentialfit/day_1/first_steps/'"
+ "'/home/pyiron/day_1/first_steps/'"
]
},
"execution_count": 4,
@@ -129,13 +129,13 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "matched-hunger",
+ "id": "upset-registrar",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'groups': ['E_V_curve', 'E_V_curve_DFT'], 'nodes': ['lammps_job', 'sphinx_job']}"
+ "{'groups': ['E_V_curve', 'E_V_curve_DFT'], 'nodes': []}"
]
},
"execution_count": 5,
@@ -149,7 +149,7 @@
},
{
"cell_type": "markdown",
- "id": "social-deadline",
+ "id": "floral-rhythm",
"metadata": {},
"source": [
"### **Creating atomic structures**"
@@ -157,7 +157,7 @@
},
{
"cell_type": "markdown",
- "id": "cathedral-death",
+ "id": "several-julian",
"metadata": {},
"source": [
"Every atomistic simulation needs an atomic structure. For more details on generating and manipulating structures, please have a look at our [structures example](https://pyiron.readthedocs.io/en/latest/source/notebooks/structures.html). In this section however, we show how to generate and manipulate bulk crystals, surfaces, etc. pyiron's structure class is derived from the popular [`ase` package](https://wiki.fysik.dtu.dk/ase/ase/build/build.html) and any `ase` function to manipulate structures can also be applied here."
@@ -166,7 +166,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "endangered-editing",
+ "id": "permanent-intelligence",
"metadata": {},
"outputs": [
{
@@ -195,13 +195,13 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "verified-support",
+ "id": "suspended-board",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d9f4e7f5fa0a4d70bfcb7fcb805a3ddc",
+ "model_id": "56ce28a3fa514410bda0b9bd78f6dcfa",
"version_major": 2,
"version_minor": 0
},
@@ -213,7 +213,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3799d6daed5c4f438f09e8fd0f1ff66a",
+ "model_id": "c697cdbffe3b40ff82f4826f200fd0cf",
"version_major": 2,
"version_minor": 0
},
@@ -233,13 +233,13 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "chinese-output",
+ "id": "aging-broadcast",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "13726dafec0e45a2854089529b295dde",
+ "model_id": "f65d82b73aba4b17b8dab3fa95fb3e7d",
"version_major": 2,
"version_minor": 0
},
@@ -260,7 +260,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "id": "mobile-tumor",
+ "id": "broke-bibliography",
"metadata": {},
"outputs": [
{
@@ -273,7 +273,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "49a3693d4b0640bdb22dc17fc0e193d0",
+ "model_id": "1f29db02f60d498eb00fd0622d2eac89",
"version_major": 2,
"version_minor": 0
},
@@ -296,13 +296,13 @@
{
"cell_type": "code",
"execution_count": 10,
- "id": "immediate-share",
+ "id": "private-spending",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "aa6674b5535b4ae8adc48584122cfc86",
+ "model_id": "cdea88312bc546fd98ff9ed2a76c24f1",
"version_major": 2,
"version_minor": 0
},
@@ -324,13 +324,13 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "dense-million",
+ "id": "metallic-fifty",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "bfc0688cd5bc493082d2d7e794b5f18b",
+ "model_id": "ad877407abbd4198b4317d3815c910e0",
"version_major": 2,
"version_minor": 0
},
@@ -352,13 +352,13 @@
{
"cell_type": "code",
"execution_count": 12,
- "id": "mexican-difference",
+ "id": "tribal-cylinder",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b17bc1e85c7e42ca8952fe74e4822c9a",
+ "model_id": "6ac856ecec5d452d929cfd309bf202f6",
"version_major": 2,
"version_minor": 0
},
@@ -385,7 +385,7 @@
},
{
"cell_type": "markdown",
- "id": "twenty-spouse",
+ "id": "cutting-pakistan",
"metadata": {},
"source": [
"### **Running an atomistic calculation using interatomic potentials (with LAMMPS)**\n",
@@ -396,7 +396,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "educated-retro",
+ "id": "color-patrol",
"metadata": {},
"outputs": [],
"source": [
@@ -406,7 +406,7 @@
},
{
"cell_type": "markdown",
- "id": "imperial-prompt",
+ "id": "forced-thumbnail",
"metadata": {},
"source": [
"Every atomistic simulation code needs an input atomic structure. We use the Cu supercell structure we created earlier"
@@ -415,7 +415,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "id": "aerial-lemon",
+ "id": "critical-joyce",
"metadata": {},
"outputs": [],
"source": [
@@ -425,7 +425,7 @@
},
{
"cell_type": "markdown",
- "id": "creative-railway",
+ "id": "clinical-closer",
"metadata": {},
"source": [
"Once the structure is assigned, an appropriate potential should also be chosen. This list of available for the structure containing Cu can be found below"
@@ -434,22 +434,22 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "restricted-yesterday",
+ "id": "pharmaceutical-sierra",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "['EAM_Dynamo_LiuLiuBorucki_1999_AlCu__MO_020851069572_000',\n",
- " 'EAM_Dynamo_MendelevKing_2008_Cu__MO_748636486270_005',\n",
- " 'EAM_Dynamo_MendelevKramerBecker_2008_Cu__MO_945691923444_005',\n",
- " 'EAM_Dynamo_MendelevKramerOtt_2009_CuZr__MO_600021860456_005',\n",
- " 'EAM_Dynamo_MendelevSordeletKramer_2007_CuZr__MO_120596890176_005',\n",
- " 'EAM_Dynamo_MishinMehlPapaconstantopoulos_2001_Cu__MO_346334655118_005',\n",
- " 'EAM_Dynamo_OnatDurukanoglu_2014_CuNi__MO_592013496703_005',\n",
- " 'EAM_Dynamo_WilliamsMishinHamilton_2006_CuAg__MO_128703483589_005',\n",
- " 'EAM_Dynamo_WuTrinkle_2009_CuAg__MO_270337113239_005',\n",
- " 'EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_CuAgAu__MO_318213562153_000']"
+ "['2016--Borovikov-V--fictional-Cu-31--LAMMPS--ipr1',\n",
+ " '2016--Borovikov-V--fictional-Cu-32--LAMMPS--ipr1',\n",
+ " '2016--Borovikov-V--fictional-Cu-33--LAMMPS--ipr1',\n",
+ " '2016--Borovikov-V--fictional-Cu-34--LAMMPS--ipr1',\n",
+ " '2016--Zhou-X-W--Al-Cu--LAMMPS--ipr2',\n",
+ " '2017--Kim-J-S--Cu-Pt--LAMMPS--ipr1',\n",
+ " '2018--Etesami-S-A--Cu--LAMMPS--ipr1',\n",
+ " '2018--Farkas-D--Fe-Ni-Cr-Co-Cu--LAMMPS--ipr2',\n",
+ " '2018--Jeong-G-U--Pd-Cu--LAMMPS--ipr1',\n",
+ " '2018--Zhou-X-W--Al-Cu-H--LAMMPS--ipr1']"
]
},
"execution_count": 15,
@@ -465,7 +465,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "id": "unauthorized-psychology",
+ "id": "saving-incident",
"metadata": {},
"outputs": [],
"source": [
@@ -475,7 +475,7 @@
},
{
"cell_type": "markdown",
- "id": "important-crowd",
+ "id": "undefined-tolerance",
"metadata": {},
"source": [
"At this stage, the computational parameters for the simulation needs to be specified. pyiron parses generic computational parameters into code specific parameters allowing for an easy transition between simulation codes"
@@ -484,7 +484,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "id": "overall-writing",
+ "id": "wrong-oxygen",
"metadata": {},
"outputs": [],
"source": [
@@ -494,7 +494,7 @@
},
{
"cell_type": "markdown",
- "id": "pediatric-guidance",
+ "id": "exterior-quest",
"metadata": {},
"source": [
"We can now see how pyiron sets-up the corresponding LAMMPS input"
@@ -503,7 +503,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "operational-galaxy",
+ "id": "discrete-myrtle",
"metadata": {},
"outputs": [
{
@@ -709,7 +709,7 @@
},
{
"cell_type": "markdown",
- "id": "equal-nylon",
+ "id": "respiratory-virus",
"metadata": {},
"source": [
"Once the `run()` commmand is called, pyiron creates necessary input files, calls the simulation code, and finally parses and stores the output."
@@ -718,14 +718,14 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "about-genre",
+ "id": "theoretical-combining",
"metadata": {},
"outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "2021-03-08 11:13:25,652 - pyiron_log - WARNING - The job lammps_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
+ "The job lammps_job was saved and received the ID: 5\n"
]
}
],
@@ -736,13 +736,13 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "headed-thinking",
+ "id": "based-backing",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'groups': ['E_V_curve', 'E_V_curve_DFT'], 'nodes': ['lammps_job', 'sphinx_job']}"
+ "{'groups': ['E_V_curve', 'E_V_curve_DFT'], 'nodes': ['lammps_job']}"
]
},
"execution_count": 20,
@@ -757,7 +757,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "republican-steps",
+ "id": "monetary-scout",
"metadata": {},
"outputs": [
{
@@ -801,386 +801,38 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>4354</td>\n",
+ " <td>5</td>\n",
" <td>finished</td>\n",
" <td>Cu108</td>\n",
" <td>lammps_job</td>\n",
" <td>/lammps_job</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/</td>\n",
- " <td>2021-03-06 16:03:40.745470</td>\n",
- " <td>2021-03-06 16:03:45.494803</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/first_steps/</td>\n",
+ " <td>2021-03-09 08:58:10.515085</td>\n",
+ " <td>2021-03-09 08:58:14.811278</td>\n",
" <td>4.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Lammps</td>\n",
- " <td>0.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>4355</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>sphinx_job</td>\n",
- " <td>/sphinx_job</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/</td>\n",
- " <td>2021-03-06 16:04:50.074383</td>\n",
- " <td>2021-03-06 16:04:56.638128</td>\n",
- " <td>6.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Sphinx</td>\n",
- " <td>2.6.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4356</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_4</td>\n",
- " <td>/job_a_3_4</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:07.639631</td>\n",
- " <td>2021-03-06 16:11:08.342303</td>\n",
- " <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Lammps</td>\n",
- " <td>0.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4357</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_5</td>\n",
- " <td>/job_a_3_5</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:08.822636</td>\n",
- " <td>2021-03-06 16:11:09.471384</td>\n",
- " <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>4358</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_6</td>\n",
- " <td>/job_a_3_6</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:09.943131</td>\n",
- " <td>2021-03-06 16:11:10.600866</td>\n",
- " <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Lammps</td>\n",
- " <td>0.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>4359</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_7</td>\n",
- " <td>/job_a_3_7</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:11.080672</td>\n",
- " <td>2021-03-06 16:11:11.753735</td>\n",
- " <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Lammps</td>\n",
- " <td>0.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>4360</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_8</td>\n",
- " <td>/job_a_3_8</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:12.228943</td>\n",
- " <td>2021-03-06 16:11:12.867039</td>\n",
- " <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Lammps</td>\n",
- " <td>0.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>4361</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_9</td>\n",
- " <td>/job_a_3_9</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:13.342478</td>\n",
- " <td>2021-03-06 16:11:13.979644</td>\n",
- " <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Lammps</td>\n",
- " <td>0.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>4362</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_4_0</td>\n",
- " <td>/job_a_4_0</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:14.465906</td>\n",
- " <td>2021-03-06 16:11:15.082557</td>\n",
- " <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Lammps</td>\n",
- " <td>0.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>4363</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_4</td>\n",
- " <td>/job_a_3_4</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/</td>\n",
- " <td>2021-03-06 16:13:46.540931</td>\n",
- " <td>2021-03-06 16:13:49.880940</td>\n",
- " <td>3.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Sphinx</td>\n",
- " <td>2.6.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>4364</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_5</td>\n",
- " <td>/job_a_3_5</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/</td>\n",
- " <td>2021-03-06 16:13:50.243705</td>\n",
- " <td>2021-03-06 16:13:53.604881</td>\n",
- " <td>3.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Sphinx</td>\n",
- " <td>2.6.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>4365</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_6</td>\n",
- " <td>/job_a_3_6</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/</td>\n",
- " <td>2021-03-06 16:13:53.999279</td>\n",
- " <td>2021-03-06 16:13:57.460432</td>\n",
- " <td>3.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Sphinx</td>\n",
- " <td>2.6.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>4366</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_7</td>\n",
- " <td>/job_a_3_7</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/</td>\n",
- " <td>2021-03-06 16:13:57.889876</td>\n",
- " <td>2021-03-06 16:14:01.894945</td>\n",
- " <td>4.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Sphinx</td>\n",
- " <td>2.6.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>4367</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_8</td>\n",
- " <td>/job_a_3_8</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/</td>\n",
- " <td>2021-03-06 16:14:02.285518</td>\n",
- " <td>2021-03-06 16:14:06.226798</td>\n",
- " <td>3.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Sphinx</td>\n",
- " <td>2.6.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>4368</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_3_9</td>\n",
- " <td>/job_a_3_9</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/</td>\n",
- " <td>2021-03-06 16:14:06.586832</td>\n",
- " <td>2021-03-06 16:14:11.549242</td>\n",
- " <td>4.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Sphinx</td>\n",
- " <td>2.6.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>4369</td>\n",
- " <td>finished</td>\n",
- " <td>Cu</td>\n",
- " <td>job_a_4_0</td>\n",
- " <td>/job_a_4_0</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/</td>\n",
- " <td>2021-03-06 16:14:12.119382</td>\n",
- " <td>2021-03-06 16:14:17.251638</td>\n",
- " <td>5.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
- " <td>Sphinx</td>\n",
- " <td>2.6.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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},
"execution_count": 21,
@@ -1194,7 +846,7 @@
},
{
"cell_type": "markdown",
- "id": "french-vertical",
+ "id": "involved-studio",
"metadata": {},
"source": [
"## Analysing a calculation"
@@ -1203,15 +855,15 @@
{
"cell_type": "code",
"execution_count": 22,
- "id": "after-experience",
+ "id": "diverse-balloon",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 1.14 s, sys: 406 ms, total: 1.55 s\n",
- "Wall time: 1.54 s\n"
+ "CPU times: user 319 ms, sys: 98.5 ms, total: 418 ms\n",
+ "Wall time: 289 ms\n"
]
}
],
@@ -1224,7 +876,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "id": "featured-locking",
+ "id": "confidential-composition",
"metadata": {},
"outputs": [
{
@@ -1245,7 +897,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "id": "interior-induction",
+ "id": "alternate-angel",
"metadata": {},
"outputs": [
{
@@ -1266,13 +918,13 @@
{
"cell_type": "code",
"execution_count": 25,
- "id": "manual-practice",
+ "id": "following-antique",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ee05400e20d94507ac3912835b6b33b5",
+ "model_id": "a4b2092e53e54dd58dde8f41fe66c1f2",
"version_major": 2,
"version_minor": 0
},
@@ -1292,13 +944,13 @@
{
"cell_type": "code",
"execution_count": 26,
- "id": "printable-soldier",
+ "id": "vertical-shade",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7f833d0e308842c2924c519c610d085d",
+ "model_id": "5a73d2800e9e46c49f088550eb282e8d",
"version_major": 2,
"version_minor": 0
},
@@ -1317,7 +969,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "id": "adjusted-movie",
+ "id": "massive-hybrid",
"metadata": {},
"outputs": [
{
@@ -1344,7 +996,7 @@
{
"cell_type": "code",
"execution_count": 28,
- "id": "bearing-receipt",
+ "id": "happy-shopper",
"metadata": {},
"outputs": [
{
@@ -1373,7 +1025,7 @@
},
{
"cell_type": "markdown",
- "id": "parallel-bunch",
+ "id": "leading-museum",
"metadata": {},
"source": [
"### **Running an atomistic calculation using DFT (with SPHInX)**"
@@ -1382,16 +1034,14 @@
{
"cell_type": "code",
"execution_count": 29,
- "id": "stone-feedback",
+ "id": "rough-shield",
"metadata": {},
"outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "/home/surendralal/programs/pyiron_base/pyiron_base/generic/inputlist.py:323: UserWarning: The input in Group changed, while the state of the job was already finished.\n",
- " warnings.warn(\n",
- "2021-03-08 11:16:38,598 - pyiron_log - WARNING - The job sphinx_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
+ "The job sphinx_job was saved and received the ID: 6\n"
]
}
],
@@ -1407,7 +1057,7 @@
{
"cell_type": "code",
"execution_count": 30,
- "id": "tropical-complement",
+ "id": "eligible-pottery",
"metadata": {},
"outputs": [
{
@@ -1428,13 +1078,13 @@
{
"cell_type": "code",
"execution_count": 31,
- "id": "superior-cisco",
+ "id": "identical-delicious",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([-5386.42735597])"
+ "array([-5386.42735492])"
]
},
"execution_count": 31,
@@ -1449,7 +1099,7 @@
{
"cell_type": "code",
"execution_count": 32,
- "id": "joined-prairie",
+ "id": "aggressive-chest",
"metadata": {},
"outputs": [
{
@@ -1470,12 +1120,12 @@
{
"cell_type": "code",
"execution_count": 33,
- "id": "flexible-horizontal",
+ "id": "forbidden-christmas",
"metadata": {},
"outputs": [
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1499,7 +1149,7 @@
},
{
"cell_type": "markdown",
- "id": "french-spare",
+ "id": "cloudy-korean",
"metadata": {},
"source": [
"## **Task 1: Energy volume curve for Al**"
@@ -1508,7 +1158,7 @@
{
"cell_type": "code",
"execution_count": 34,
- "id": "resistant-vegetation",
+ "id": "nearby-queen",
"metadata": {},
"outputs": [],
"source": [
@@ -1519,7 +1169,7 @@
{
"cell_type": "code",
"execution_count": 35,
- "id": "bibliographic-chancellor",
+ "id": "boolean-spectrum",
"metadata": {},
"outputs": [],
"source": [
@@ -1530,13 +1180,13 @@
{
"cell_type": "code",
"execution_count": 36,
- "id": "regulated-marriage",
+ "id": "fiscal-copyright",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(-345.776978906969, 1337.89883611173)"
+ "(-345.776978906089, 1337.89883612206)"
]
},
"execution_count": 36,
@@ -1551,28 +1201,22 @@
{
"cell_type": "code",
"execution_count": 37,
- "id": "blond-abortion",
+ "id": "satellite-table",
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2021-03-08 11:19:23,879 - pyiron_log - WARNING - The job job_a_3_4 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:19:26,309 - pyiron_log - WARNING - The job job_a_3_5 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:19:29,036 - pyiron_log - WARNING - The job job_a_3_6 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:19:31,081 - pyiron_log - WARNING - The job job_a_3_7 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:19:33,948 - pyiron_log - WARNING - The job job_a_3_8 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:19:36,713 - pyiron_log - WARNING - The job job_a_3_9 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:19:38,542 - pyiron_log - WARNING - The job job_a_4_0 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 14.2 s, sys: 6.02 s, total: 20.2 s\n",
- "Wall time: 16.5 s\n"
+ "The job job_a_3_4 was saved and received the ID: 7\n",
+ "The job job_a_3_5 was saved and received the ID: 8\n",
+ "The job job_a_3_6 was saved and received the ID: 9\n",
+ "The job job_a_3_7 was saved and received the ID: 10\n",
+ "The job job_a_3_8 was saved and received the ID: 11\n",
+ "The job job_a_3_9 was saved and received the ID: 12\n",
+ "The job job_a_4_0 was saved and received the ID: 13\n",
+ "CPU times: user 6.9 s, sys: 4.42 s, total: 11.3 s\n",
+ "Wall time: 9.57 s\n"
]
}
],
@@ -1593,7 +1237,7 @@
{
"cell_type": "code",
"execution_count": 38,
- "id": "imported-birmingham",
+ "id": "regular-project",
"metadata": {},
"outputs": [
{
@@ -1614,7 +1258,7 @@
{
"cell_type": "code",
"execution_count": 39,
- "id": "demographic-publicity",
+ "id": "maritime-storage",
"metadata": {},
"outputs": [
{
@@ -1658,17 +1302,17 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>4356</td>\n",
+ " <td>7</td>\n",
" <td>finished</td>\n",
" <td>Cu</td>\n",
" <td>job_a_3_4</td>\n",
" <td>/job_a_3_4</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:07.639631</td>\n",
- " <td>2021-03-06 16:11:08.342303</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/first_steps/E_V_curve/</td>\n",
+ " <td>2021-03-09 08:58:20.864847</td>\n",
+ " <td>2021-03-09 08:58:21.319587</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1676,17 +1320,17 @@
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>4357</td>\n",
+ " <td>8</td>\n",
" <td>finished</td>\n",
" <td>Cu</td>\n",
" <td>job_a_3_5</td>\n",
" <td>/job_a_3_5</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:08.822636</td>\n",
- " <td>2021-03-06 16:11:09.471384</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/first_steps/E_V_curve/</td>\n",
+ " <td>2021-03-09 08:58:22.007631</td>\n",
+ " <td>2021-03-09 08:58:22.417699</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1694,17 +1338,17 @@
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>4358</td>\n",
+ " <td>9</td>\n",
" <td>finished</td>\n",
" <td>Cu</td>\n",
" <td>job_a_3_6</td>\n",
" <td>/job_a_3_6</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:09.943131</td>\n",
- " <td>2021-03-06 16:11:10.600866</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/first_steps/E_V_curve/</td>\n",
+ " <td>2021-03-09 08:58:23.342237</td>\n",
+ " <td>2021-03-09 08:58:23.719938</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1712,17 +1356,17 @@
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>4359</td>\n",
+ " <td>10</td>\n",
" <td>finished</td>\n",
" <td>Cu</td>\n",
" <td>job_a_3_7</td>\n",
" <td>/job_a_3_7</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:11.080672</td>\n",
- " <td>2021-03-06 16:11:11.753735</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/first_steps/E_V_curve/</td>\n",
+ " <td>2021-03-09 08:58:24.608633</td>\n",
+ " <td>2021-03-09 08:58:25.030868</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1730,17 +1374,17 @@
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>4360</td>\n",
+ " <td>11</td>\n",
" <td>finished</td>\n",
" <td>Cu</td>\n",
" <td>job_a_3_8</td>\n",
" <td>/job_a_3_8</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:12.228943</td>\n",
- " <td>2021-03-06 16:11:12.867039</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/first_steps/E_V_curve/</td>\n",
+ " <td>2021-03-09 08:58:26.178603</td>\n",
+ " <td>2021-03-09 08:58:26.620330</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1748,17 +1392,17 @@
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
- " <td>4361</td>\n",
+ " <td>12</td>\n",
" <td>finished</td>\n",
" <td>Cu</td>\n",
" <td>job_a_3_9</td>\n",
" <td>/job_a_3_9</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:13.342478</td>\n",
- " <td>2021-03-06 16:11:13.979644</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/first_steps/E_V_curve/</td>\n",
+ " <td>2021-03-09 08:58:27.666351</td>\n",
+ " <td>2021-03-09 08:58:28.127485</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1766,17 +1410,17 @@
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
- " <td>4362</td>\n",
+ " <td>13</td>\n",
" <td>finished</td>\n",
" <td>Cu</td>\n",
" <td>job_a_4_0</td>\n",
" <td>/job_a_4_0</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/</td>\n",
- " <td>2021-03-06 16:11:14.465906</td>\n",
- " <td>2021-03-06 16:11:15.082557</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/first_steps/E_V_curve/</td>\n",
+ " <td>2021-03-09 08:58:29.467928</td>\n",
+ " <td>2021-03-09 08:58:29.922692</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1787,41 +1431,41 @@
"</div>"
],
"text/plain": [
- " id status chemicalformula job subjob projectpath \\\n",
- "0 4356 finished Cu job_a_3_4 /job_a_3_4 /home/surendralal/ \n",
- "1 4357 finished Cu job_a_3_5 /job_a_3_5 /home/surendralal/ \n",
- "2 4358 finished Cu job_a_3_6 /job_a_3_6 /home/surendralal/ \n",
- "3 4359 finished Cu job_a_3_7 /job_a_3_7 /home/surendralal/ \n",
- "4 4360 finished Cu job_a_3_8 /job_a_3_8 /home/surendralal/ \n",
- "5 4361 finished Cu job_a_3_9 /job_a_3_9 /home/surendralal/ \n",
- "6 4362 finished Cu job_a_4_0 /job_a_4_0 /home/surendralal/ \n",
+ " id status chemicalformula job subjob projectpath \\\n",
+ "0 7 finished Cu job_a_3_4 /job_a_3_4 /home/pyiron/ \n",
+ "1 8 finished Cu job_a_3_5 /job_a_3_5 /home/pyiron/ \n",
+ "2 9 finished Cu job_a_3_6 /job_a_3_6 /home/pyiron/ \n",
+ "3 10 finished Cu job_a_3_7 /job_a_3_7 /home/pyiron/ \n",
+ "4 11 finished Cu job_a_3_8 /job_a_3_8 /home/pyiron/ \n",
+ "5 12 finished Cu job_a_3_9 /job_a_3_9 /home/pyiron/ \n",
+ "6 13 finished Cu job_a_4_0 /job_a_4_0 /home/pyiron/ \n",
"\n",
- " project \\\n",
- "0 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/ \n",
- "1 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/ \n",
- "2 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/ \n",
- "3 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/ \n",
- "4 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/ \n",
- "5 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/ \n",
- "6 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/ \n",
+ " project timestart \\\n",
+ "0 day_1/first_steps/E_V_curve/ 2021-03-09 08:58:20.864847 \n",
+ "1 day_1/first_steps/E_V_curve/ 2021-03-09 08:58:22.007631 \n",
+ "2 day_1/first_steps/E_V_curve/ 2021-03-09 08:58:23.342237 \n",
+ "3 day_1/first_steps/E_V_curve/ 2021-03-09 08:58:24.608633 \n",
+ "4 day_1/first_steps/E_V_curve/ 2021-03-09 08:58:26.178603 \n",
+ "5 day_1/first_steps/E_V_curve/ 2021-03-09 08:58:27.666351 \n",
+ "6 day_1/first_steps/E_V_curve/ 2021-03-09 08:58:29.467928 \n",
"\n",
- " timestart timestop totalcputime \\\n",
- "0 2021-03-06 16:11:07.639631 2021-03-06 16:11:08.342303 0.0 \n",
- "1 2021-03-06 16:11:08.822636 2021-03-06 16:11:09.471384 0.0 \n",
- "2 2021-03-06 16:11:09.943131 2021-03-06 16:11:10.600866 0.0 \n",
- "3 2021-03-06 16:11:11.080672 2021-03-06 16:11:11.753735 0.0 \n",
- "4 2021-03-06 16:11:12.228943 2021-03-06 16:11:12.867039 0.0 \n",
- "5 2021-03-06 16:11:13.342478 2021-03-06 16:11:13.979644 0.0 \n",
- "6 2021-03-06 16:11:14.465906 2021-03-06 16:11:15.082557 0.0 \n",
+ " timestop totalcputime computer hamilton \\\n",
+ "0 2021-03-09 08:58:21.319587 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "1 2021-03-09 08:58:22.417699 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "2 2021-03-09 08:58:23.719938 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "3 2021-03-09 08:58:25.030868 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "4 2021-03-09 08:58:26.620330 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "5 2021-03-09 08:58:28.127485 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "6 2021-03-09 08:58:29.922692 0.0 pyiron@jupyter-janssen#1 Lammps \n",
"\n",
- " computer hamilton hamversion parentid masterid \n",
- "0 pyiron@cmdell17#1 Lammps 0.1 None None \n",
- "1 pyiron@cmdell17#1 Lammps 0.1 None None \n",
- "2 pyiron@cmdell17#1 Lammps 0.1 None None \n",
- "3 pyiron@cmdell17#1 Lammps 0.1 None None \n",
- "4 pyiron@cmdell17#1 Lammps 0.1 None None \n",
- "5 pyiron@cmdell17#1 Lammps 0.1 None None \n",
- "6 pyiron@cmdell17#1 Lammps 0.1 None None "
+ " hamversion parentid masterid \n",
+ "0 0.1 None None \n",
+ "1 0.1 None None \n",
+ "2 0.1 None None \n",
+ "3 0.1 None None \n",
+ "4 0.1 None None \n",
+ "5 0.1 None None \n",
+ "6 0.1 None None "
]
},
"execution_count": 39,
@@ -1836,15 +1480,15 @@
{
"cell_type": "code",
"execution_count": 40,
- "id": "composed-architecture",
+ "id": "operating-strike",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 7.06 s, sys: 2.73 s, total: 9.8 s\n",
- "Wall time: 8.18 s\n"
+ "CPU times: user 2.63 s, sys: 1.13 s, total: 3.76 s\n",
+ "Wall time: 2.46 s\n"
]
},
{
@@ -1891,15 +1535,15 @@
{
"cell_type": "code",
"execution_count": 41,
- "id": "challenging-knife",
+ "id": "seventh-optics",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 250 ms, sys: 46.9 ms, total: 297 ms\n",
- "Wall time: 322 ms\n"
+ "CPU times: user 62.5 ms, sys: 7.75 ms, total: 70.2 ms\n",
+ "Wall time: 67.3 ms\n"
]
},
{
@@ -1945,7 +1589,7 @@
},
{
"cell_type": "markdown",
- "id": "chemical-survival",
+ "id": "foster-portland",
"metadata": {},
"source": [
"## **Task 2: E-V curves for DFT**"
@@ -1954,30 +1598,114 @@
{
"cell_type": "code",
"execution_count": 42,
- "id": "retained-monkey",
+ "id": "legal-mention",
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job job_a_3_4 was saved and received the ID: 14\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/pyiron_base/generic/hdfio.py:718: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+ " np.array([np.array(v) for v in value]),\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job job_a_3_5 was saved and received the ID: 15\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/pyiron_base/generic/hdfio.py:718: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+ " np.array([np.array(v) for v in value]),\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job job_a_3_6 was saved and received the ID: 16\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/pyiron_base/generic/hdfio.py:718: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+ " np.array([np.array(v) for v in value]),\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job job_a_3_7 was saved and received the ID: 17\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/pyiron_base/generic/hdfio.py:718: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+ " np.array([np.array(v) for v in value]),\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job job_a_3_8 was saved and received the ID: 18\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/pyiron_base/generic/hdfio.py:718: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+ " np.array([np.array(v) for v in value]),\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job job_a_3_9 was saved and received the ID: 19\n"
+ ]
+ },
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/home/surendralal/programs/pyiron_base/pyiron_base/generic/inputlist.py:323: UserWarning: The input in Group changed, while the state of the job was already finished.\n",
- " warnings.warn(\n",
- "2021-03-08 11:19:50,455 - pyiron_log - WARNING - The job job_a_3_4 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:19:51,968 - pyiron_log - WARNING - The job job_a_3_5 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:19:54,008 - pyiron_log - WARNING - The job job_a_3_6 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:19:55,854 - pyiron_log - WARNING - The job job_a_3_7 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:19:57,682 - pyiron_log - WARNING - The job job_a_3_8 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:19:58,806 - pyiron_log - WARNING - The job job_a_3_9 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:20:00,681 - pyiron_log - WARNING - The job job_a_4_0 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
+ "/opt/conda/lib/python3.8/site-packages/pyiron_base/generic/hdfio.py:718: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+ " np.array([np.array(v) for v in value]),\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 9.27 s, sys: 2.62 s, total: 11.9 s\n",
- "Wall time: 12.1 s\n"
+ "The job job_a_4_0 was saved and received the ID: 20\n",
+ "CPU times: user 4.8 s, sys: 4.28 s, total: 9.07 s\n",
+ "Wall time: 28.1 s\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.8/site-packages/pyiron_base/generic/hdfio.py:718: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+ " np.array([np.array(v) for v in value]),\n"
]
}
],
@@ -2001,21 +1729,21 @@
{
"cell_type": "code",
"execution_count": 43,
- "id": "descending-insurance",
+ "id": "similar-anderson",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 531 ms, sys: 125 ms, total: 656 ms\n",
- "Wall time: 696 ms\n"
+ "CPU times: user 137 ms, sys: 9.99 ms, total: 147 ms\n",
+ "Wall time: 144 ms\n"
]
},
{
"data": {
"text/plain": [
- "<matplotlib.legend.Legend at 0x7f73835ac4c0>"
+ "<matplotlib.legend.Legend at 0x7efbf08da6d0>"
]
},
"execution_count": 43,
@@ -2024,7 +1752,7 @@
},
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2057,7 +1785,7 @@
},
{
"cell_type": "markdown",
- "id": "coordinated-partner",
+ "id": "capable-ballot",
"metadata": {},
"source": [
"## **Advanced pyiron: Automated workflows and analysis tools**"
@@ -2066,15 +1794,13 @@
{
"cell_type": "code",
"execution_count": 44,
- "id": "cooperative-documentary",
+ "id": "facial-kuwait",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "['Cu_Mendelev_eam',\n",
- " '2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2',\n",
- " '1985--Foiles-S-M--Ni-Cu--LAMMPS--ipr1']"
+ "['Cu-ace', 'Cu-atomicrex-df1-0-13', 'Cu-atomicrex-df1-68-22']"
]
},
"execution_count": 44,
@@ -2092,7 +1818,7 @@
{
"cell_type": "code",
"execution_count": 45,
- "id": "detailed-empire",
+ "id": "packed-montgomery",
"metadata": {},
"outputs": [],
"source": [
@@ -2103,24 +1829,72 @@
{
"cell_type": "code",
"execution_count": 46,
- "id": "meaningful-europe",
+ "id": "defined-ceramic",
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2021-03-08 11:20:04,503 - pyiron_log - WARNING - The job murn_ref_Cu_Mendelev_eam is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:20:06,613 - pyiron_log - WARNING - The job murn_ref_2004__Zhou_X_W__Cu_Ag_Au__LAMMP is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-08 11:20:09,169 - pyiron_log - WARNING - The job murn_ref_1985__Foiles_S_M__Ni_Cu__LAMMPS is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 5.44 s, sys: 2.2 s, total: 7.64 s\n",
- "Wall time: 6.29 s\n"
+ "The job murn_ref_Cu_ace was saved and received the ID: 21\n",
+ "The job strain_0_9 was saved and received the ID: 22\n",
+ "The job strain_0_925 was saved and received the ID: 23\n",
+ "The job strain_0_95 was saved and received the ID: 24\n",
+ "The job strain_0_975 was saved and received the ID: 25\n",
+ "The job strain_1_0 was saved and received the ID: 26\n",
+ "The job strain_1_025 was saved and received the ID: 27\n",
+ "The job strain_1_05 was saved and received the ID: 28\n",
+ "The job strain_1_075 was saved and received the ID: 29\n",
+ "The job strain_1_1 was saved and received the ID: 30\n",
+ "job_id: 22 finished\n",
+ "job_id: 23 finished\n",
+ "job_id: 24 finished\n",
+ "job_id: 25 finished\n",
+ "job_id: 26 finished\n",
+ "job_id: 27 finished\n",
+ "job_id: 28 finished\n",
+ "job_id: 29 finished\n",
+ "job_id: 30 finished\n",
+ "The job murn_ref_Cu_atomicrex_df1_0_13 was saved and received the ID: 31\n",
+ "The job strain_0_9 was saved and received the ID: 32\n",
+ "The job strain_0_925 was saved and received the ID: 33\n",
+ "The job strain_0_95 was saved and received the ID: 34\n",
+ "The job strain_0_975 was saved and received the ID: 35\n",
+ "The job strain_1_0 was saved and received the ID: 36\n",
+ "The job strain_1_025 was saved and received the ID: 37\n",
+ "The job strain_1_05 was saved and received the ID: 38\n",
+ "The job strain_1_075 was saved and received the ID: 39\n",
+ "The job strain_1_1 was saved and received the ID: 40\n",
+ "job_id: 32 finished\n",
+ "job_id: 33 finished\n",
+ "job_id: 34 finished\n",
+ "job_id: 35 finished\n",
+ "job_id: 36 finished\n",
+ "job_id: 37 finished\n",
+ "job_id: 38 finished\n",
+ "job_id: 39 finished\n",
+ "job_id: 40 finished\n",
+ "The job murn_ref_Cu_atomicrex_df1_68_22 was saved and received the ID: 41\n",
+ "The job strain_0_9 was saved and received the ID: 42\n",
+ "The job strain_0_925 was saved and received the ID: 43\n",
+ "The job strain_0_95 was saved and received the ID: 44\n",
+ "The job strain_0_975 was saved and received the ID: 45\n",
+ "The job strain_1_0 was saved and received the ID: 46\n",
+ "The job strain_1_025 was saved and received the ID: 47\n",
+ "The job strain_1_05 was saved and received the ID: 48\n",
+ "The job strain_1_075 was saved and received the ID: 49\n",
+ "The job strain_1_1 was saved and received the ID: 50\n",
+ "job_id: 42 finished\n",
+ "job_id: 43 finished\n",
+ "job_id: 44 finished\n",
+ "job_id: 45 finished\n",
+ "job_id: 46 finished\n",
+ "job_id: 47 finished\n",
+ "job_id: 48 finished\n",
+ "job_id: 49 finished\n",
+ "job_id: 50 finished\n",
+ "CPU times: user 31.3 s, sys: 16.5 s, total: 47.7 s\n",
+ "Wall time: 39.4 s\n"
]
}
],
@@ -2149,12 +1923,12 @@
{
"cell_type": "code",
"execution_count": 47,
- "id": "signal-agreement",
+ "id": "historical-output",
"metadata": {},
"outputs": [
{
"data": {
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8ebp48WItXbq0fvXVV6qqeu655+rIkSNVVbVly5Y6ffp0VVW9/fbb9brrrvt7nwMGDFBV1SFDhmidOnV0xYoVumXLFq1Xr57+/vvvqqpauXJlVVXdvn27rlu3TlVVV61apU2aNNGdO3cWSBPvWIePObM0gWuslVCiiGwafMYZUL26W/fLLzBwYPH6/jLGlEwNGjSgY8eOAFx00UVMmTKFffbZh/322w+AHj168NFHHxXYpnLlyhxzzDFMmDCBRYsWsX37dlq2bAnAPvvsQ+vWrQFo164dS5YsYd26daxdu5ajjjoq6j67du0KQMuWLWnevDl16tShfPnyNG7cmGXLlhV4b1Xl1ltvpVWrVnTp0oWff/6ZX3/9NfUHxmMBJUK050wqVIALLwynmTu3+B1KGmNKnqI2q+3VqxfDhw/nhRde4LLLLvt7efny5f+eLl26NDtCLYDiCG1TqlSpAtuXKlVql+1HjRrFqlWrmD17NnPmzKF27dpp7W3AAopPvIcWfecAr70GHTpYUDEmCOms9CrMTz/9xGeffQbAmDFj6NKlC0uWLOGHH34AYOTIkX+XLPwOOeQQli1bxujRo7nQ/+s0iurVq7P77rvz8ccfx91nItatW8eee+5J2bJlmTZtGkuXJtQLfZFZQPEU9gR8hw7QrJmb3rgRXn89NV3fG2NKjmbNmjFixAhatWrFH3/8Qf/+/XnhhRc499xzadmyJaVKlaJPnz5Rtz3vvPPo2LEju+++e6HvM2LECG688UZatWrFnDlzGDRoUJHy2717d2bNmkX79u0ZNWoUBxxwQJH2k7BEbrTkyivWTXn/Dfh4Hnww/FumU6fktzfGFE20G8WZtnjxYm3evHmRtz/llFN08uTJKcxRethN+WJIpm+uiy+G0qXd9Ecfwfffu2krqRhjYlm7di377bcfFStW5Nhjjw06O2mV9wFl5szE++aqUwdOPjk8/8IL4elQUJk5M/V5NMYEr1GjRsyfPz/p7XbbbTe+++47XnvttTTkKrvkfUAZMCC57lR69gxPDx8e7pYF3H4GDEhZ1owxPprIXXNTLMU9xnkfUJJ1yilQu7abXrkS3n8/2PwYkw8qVKjA6tWrLaikkaobD6VChQpF3of1NpyksmVdF/YPP+zmn3vOBRljTPrUr1+f5cuXs2rVqqCzktNCIzYWleRTxG/fvr2movfPRYvCTYjLlIHly8OlFmOMyTUiMltV2xeWzqq8iuCAAyDUO/SOHfDSS8HmxxhjskEgAUVEBovIPBGZIyKTRKRunLSlReQrEZngW9ZaRD73tp8lIgdnJudh//hHePrZZxN7ytYYY3JZUCWUh1W1laq2BiYA8R4DvQ5YGLHsIeAub/tB3nxGnXcehManWbQIPvkk0zkwxpjsEkhAUdU/fbOVgai/70WkPnAK8GzkLoBq3nR1YEWq81iYKlUKdhj5zDOZzoExxmSXwO6hiMi9IrIM6E7sEsoQYACwM2J5P+Bhb/tHgFvSlM24Lr88PP3aa7B2bRC5MMaY7JC2gCIik0VkfpTX6QCqOlBVGwCjgL5Rtj8V+E1VZ0fZ/ZVAf2/7/sBzcfLR27vPMivVTQ7btwdvKAM2b4ZRo1K6e2OMKVECbzYsIg2Bd1S1RcTy+4GLgR1ABVwV1xuqepGIrAN2U1UVN0DBOlWtFrnvSKlqNuz35JNumGCAVq1gzhwo4pAJxhiTlbK62bCINPXNdgUWRaZR1VtUtb6qNgIuAKaq6kXe6hVAaICAY4Dv05jduLp3h4oV3fS8edaXlzEmfwV1D+UBr/prHnA8riUXIlJXRCYmsP3lwL9EZC5wH9A7fVmNr3p11+IrxG7OG2PyVeBVXpmUjiovcE2GjzjCTVeuDCtWQLVCK+CMMaZkyOoqr1xz+OHQvLmb3rjRbs4bY/KTBZQUEAH/qJ9PP21Pzhtj8o8FlBS56KLwzfm5c+GLL4LNjzHGZJoFlBTZbbeCT84//XRgWTHGmEBYQEkhf7XXyy/DmjXB5cUYYzLNAkoKtW8Pbdq46S1bYOTIYPNjjDGZZAElhSJvzj/1lN2cN8bkDwsoKdatG1St6qYXLoQPPww2P8YYkykWUFKsShW4+OLw/NChweXFGGMyyQJKGlx1VXh63Dj35LwxxuQ6Cyhp0Lw5HOV1XfnXXzBsWLD5McaYTLCAkib+UsqwYbB9e3B5McaYTLCAkiZnngl77eWmV66E8eODzY8xxqSbBZQ0KVsWevs61beb88aYXGcBJY0uvxxKl3bT06fD/PmBZscYY9LKAkoa1a8PZ5wRnn/iicCyYowxaWcBJc2uuSY8PXKk9e9ljMldFlDSrFMnaNXKTW/aBM8/H2x+jDEmXSygpJlIwVLK0KHu2RRjjMk1FlAyoFs32H13N714MUycGGx+jDEmHSygZEClStCrV3j+8ceDy4sxxqRLIAFFRAaLyDwRmSMik0Skbpy0pUXkKxGZ4Ft2kIh8JiJfi8jbIlItMzkvuquuglLe0f7gA1iwINj8GGNMqgVVQnlYVVupamtgAjAoTtrrgIURy54FblbVlsA44Ma05DKFGjWC008Pzz/2WGBZMcaYtAgkoKjqn77ZykDUYahEpD5wCi6A+O0PfORNfwCcneo8pkO/fuHpF1+E1asDy4oxxqRcYPdQROReEVkGdCd2CWUIMADYGbF8PtDVmz4XaBDnfXqLyCwRmbVq1ariZbqYjjwyPETw5s3wzDOBZscYY1IqbQFFRCaLyPwor9MBVHWgqjYARgF9o2x/KvCbqs6OsvuewNUiMhuoCmyLlQ9VHaaq7VW1fa1atVLy2YpKpGAp5YknrBdiY0zuEA140HMRaQi8o6otIpbfD1wM7AAqANWAN1T1ooh0+wEvqerBhb1X+/btddasWSnLe1Fs3QoNG8Kvv7r5MWPgggsCzZIxxsQlIrNVtX1h6YJq5dXUN9sVWBSZRlVvUdX6qtoIuACYGgomIrKn97cUcBvwVNoznSLlyxccK2XIkMCyYowxKRXUPZQHvOqvecDxuJZciEhdEUnksb8LReQ7XCBaAbyQvqymXp8+UK6cm/7iC/jss2DzY4wxqRB4lVcmZUOVV0jPnvCCFwbPPhvGjg02P8YYE0tWV3kZuP768PQbb8CPPwaXF2OMSQULKAFp0QJOOMFNq9q9FGNMyWcBJUD//Gd4+vnn4Y8/gsuLMcYUlwWUAHXpUnCslKefDjY/xhhTHBZQAiRS8F7K44+751SMMaYksoASsAsvhDp13PTKlTB6dLD5McaYorKAErBy5eC668LzDz8MOyN7LjPGmBLAAkoWuOIKqFrVTS9cCO+8E2x+jDGmKCygZIHddnNBJeShhwLLijHGFJkFlCzRrx+ULeumZ8yATz8NNDvGGJM0CyhZol49uMjXj7KVUowxJY0FlCxyww3h6fHj3f0UY4wpKSygZJEDD4TTTgvPWynFGFOSWEDJMrfcEp5+6SVYujS4vBhjTDIsoGSZww6Do4920zt2wCOPBJodY4xJmAWULOQvpTz7LPz2W3B5McZkt4cegmnTUrOvadOKV9VuASULHXcctGvnprdssa7tjTGxdegA551X/KAybZrbT4cORd9H3IAiIoeJyFARmSciq0TkJxGZKCJXi0j1or+tiUcEbr01PD90KKxbF1x+jDHZq3NnePXV4gWVUDB59VW3v6KKGVBE5F2gF/A+cCJQBzgQuA2oAIwXka5Ff2sTzxlnwAEHuOk//4Qnngg0O8aYLFacoJKqYAJxxpQXkZqq+nvcjRNIk02yaUz5RIwYAZde6qb32AOWLAn3+WWMMZGSDQ6Jpk/FmPJ3isjh8TYuScGkJOrWDfbZx03/8Qf85z/B5scYk92SKamksmQSEi+gfA/8S0SWiMiDItI6NW8JIjLYuy8zR0QmiUjdGOmWiMjXXrpZvuV7iMgHIvK993f3VOUtm5QtW7DF17/+5UZ2NMaYWBIJKukIJhAnoKjq/6nqYcBRwB/ACyKyUEQGich+xXzfh1W1laq2BiYAg+Kk7ayqrSOKWzcDU1S1KTDFm89JPXpAgwZu+rffYNiwYPNjjMl+8YJKuoIJJNBsWFWXquqDqtoG6AacCRSrlylV/dM3WxmIfiMnttOBEd70COCM4uQnm5UrBzf7wuVDD7mmxMYYE48/qIzwrpbpDCaQQEARkbIicpqIjALeBb4Dzi7uG4vIvSKyDOhO7BKKApNEZLaI9PYtr62qKwG8v3vGeZ/eIjJLRGatWrWquNnOGP/DSj17Fhwm+Nlnk9tXcR9WMsaUTJ07w5lnusY9XbqkN5hA/GbDx4nI88ByoDcwEWiiquer6puF7VhEJovI/Civ0wFUdaCqNgBGAX1j7KajqrYFTgKuFpFOyX08UNVhqtpeVdvXqlUr2c0D439YqUIFGDAgvO7++xMvpaTiYSVjTMn01VcwfLibnjIFDj00fcEE4pdQbgU+A5qp6mmqOkpVNya6Y1XtoqotorzGRyQdTYwSj6qu8P7+BowDDvZW/SoidQC8vznXOUlkHegVV8Bee7l1K1bAM88Uvo90F2+NMdlr82Y3xtL27W6+bl34/PPUddMSTbyb8p1V9RlV/UNEjhCRywBEpJaI7FOcNxWRpr7ZrsCiKGkqi0jV0DRwPDDfW/0W0MOb7gFEBqmc4A8qn39e8F7K/fe7EyYWCybG5LdbboEFC9x0+fIwfXrxn6gvlKrGfQF3AG8D33nzdYFPCtuukH2+jgsO87x91/Pte6I33RiY672+AQb6tq+Ba931vfd3j0Tet127dloSTZ2qWrOm6rvvqtapowruNWRI/PRTp2Y2n8aY7DBpUvg6AapPPRVeV5TrAzBLE7m2F5oA5gACfOVbNi+RnWfbq6QGFNXwSdC3b/gk2Wsv1U2boqezYGJMfvr9d9UaNcLXiVNPVd25s2CaZK8TiQaURHob3ubtUOHv6ieTYaHqr5dfhpo13bJffin49LxVcxmT31RdP4CrV7v5WrVcq1CRgulS0aFkNIkElFdF5GlgNxG5HJgMJHBL2KRa6CTwt/B64AHYsMGCiTHGtQadMSM8//zzULt29LTpCCqJPNj4CDAWd99jf2CQqj6emrc3yercGcaOhVLef27VKujXz4KJMflu5MiCI7xedRWcemr8bVIdVOL1Niwaa2USabJJSettOJ4bbwyfPCIwfjycdlqweTLGBOODD+Dkk92w4eCGvpg9GypVSmz7wmo4UtHb8DQRuUZE9o7YcTkROUZERhBuumsy7P77XZf24OpNv/wy2PwYY4IxbRqcfno4mJQtC6NHJx5MIHUllXgB5UTgL2CMiKwQkQUishjXVPdC4FFVHV70tzbF8fHH4QeWwJVWSlDPMsaYFBkzpuAzaffdB23aJL+fUFCZObPoeYlZ5VUgkUhZoCawWVXXFv3tgpUrVV6h4unLL7v7J/O9xz3POQdeey3QrBljMuj33+Ggg1zvGQDHHQfvvRe+x5oqqajy+puqblfVlSU5mOQKf13nscfC4MHhdWPHuiBjjMl9qtCrVziY1KzpehVOdTBJRoBvbZIV7cbZ6ae7Dt9CLr00vX31GGOyw5NPusY4IcOHh3slD4oFlBIiVisMEfcsSsi2bXD22RZUjMllc+fCP/8Znr/mGjjllODyE5LIeCh9c3WI3ZKisCZ9Rx0FJ57oplWhWbM0dwBnjAnMxo1w/vmwdaubP+ig7BnvKJESyl7ATBF5VUROFIl8iN+kU6JPwN9/f3j6009h0CALKsbkomuvhW+/ddOVKsErr7gxk7JBIk/K3wY0BZ4DLgW+F5H7RKRJmvOW95LpTqV1a+jWLTz/8svuRLOgYkzuGD3adacSMnQo7L9/cPmJlGgrLwV+8V47gN2BsSKSJQWt3FOUvrkGD3YPNYErpaxdm4HxD4wxGfH9926gvZBu3aBHlj1ansg9lGtFZDbwEPAJ0FJVrwTakYKx5U10M2cm3zdX48Zw9dXh+Ztugo4di/+wkjEmWFu2uB+GGza4+X33haee2rUX4aAV+mCjiNwNPKeqS6Osa6aqC9OVuVTLlQcb41m9Gpo0gXXr3Pxjj7kWIMaYkqtvX1e9BVCunBvBtShPwxdVKh9sHAKsF5E9fK+yACUpmOSLGjXgttvC83fdFQ4uxpiSZ+zYcDABePTRzAaTZCQSUP4LrAK+w/XjtQpYLCL/FZF26cycKZq+faFhQze9enXBFmDGmJLjhx+gZ8/w/Nlnw5VXBpefwiQSUN4DTlbVmqpaAzgJeBW4CngynZkzRVOhgusgLmTIEFi8OLDsGGOKYMsWOPdcWL/ezTduDM89l333TfwSCSjtVfX90IyqTgI6qernQPm05cwUywUXwMEHu+mtW90NemNMydGvH8yZ46bLlXMdv1avHmSOCpdIQPlDRG4SkYbeawCwRkRKAzuL8qYiMlhE5onIHBGZJCJ1Y6RbIiJfe+lm+ZafKyLfiMhOESn0RlE+KlXK1bWGvPaa6/LeGJP9Ro2Cp58Ozw8ZAm3bBpadhCUSULoB9YE3vVcDb1lp4Lwivu/DqtpKVVsDE4BBcdJ2VtXWES0M5gNnAR8V8f3zwuGHu5JKSP/+sLNIPwGMMZnyzTfQu3d4/vzzoU+f4PKTjLgBxSuFDFHVa1S1jfe6RlVXqeo2Vf2hKG+qqn/6ZisDSQ0jrKoLVfXborx3vnnggXC3DLNnu3GnjTHZaf16d+N90yY3v//+8Mwz2X3fxC9uQFHVv4BaIlIu1W8sIveKyDKgO7FLKApMEpHZItI7RhoTR8OGBXslvfnm8E0+Y0z2UIV//KNgP12vvw5Vqwabr2QkUuW1BPhERG4XketDr8I2EpHJIjI/yut0AFUdqKoNgFFA3xi76aiqbXEty64WkU6JfawC+egtIrNEZNaqPB0j9+abw+Mk/PJLwUG5jDHZ4bHHCo64OmwYNG8eXH6KIpGAsgJ3n6MUUNX3iktVu6hqiyiv8RFJRxOjCxdVXeH9/Q0YBxycQH4j9zFMVduravtatWolu3lOqFKlYPfWQ4aEfwUZY4L38cdwww3h+SuvhO7dg8tPUZUpLIGq3gUgIpVVdWMq3lREmqrq995sV2BRlDSVgVKqut6bPh64OxXvn4+6d3d9/3zyCWzf7pokTpxYcupmjclVK1e6frp27HDzBx9csIVmSZJI55CHicgCYKE3f5CIFPeBxge86q95uEBxnbfvuiIy0UtTG5ghInOBL4F3VPU9L92ZIrIcOAx4R0Te3/UtjJ8IPP54OIC89x5MmBBsnozJd9u3u4cXf/nFzdes6bpaKV9Cn/BLpHPIL4BzgLdUtY23bL6qtshA/lIqHzqHLEyfPuH27fvs45ooVqwYbJ6MyVfXXut+6IF7dmzSJDj22GDzFE0qO4dEVZdFLPqrSLkygbv3XtjdG9B58eKC49EbYzJnxIhwMAHXXVI2BpNkJBJQlonI4YCKSDkRuQGv+suUPDVqFAwiDzzgBu4xxmTO7NkFB8s65xwYMCC4/KRKIgGlD3A1UA9YDrT25k0J1atXuJ+vbdtc78SF1HwaY1Jk1So46yzXxx64psEvvJAbDWQSGVP+d1Xtrqq1VXVPVb1IVVdnInMmPUqVgv/8x/0FV287dmyweTImH4Ruwv/0k5uvXh3GjXNN+3NBIq28aonIrSIyTESeD70ykTmTPm3bwlVXhef79YM//4yZ3BiTAv37w4cfumkR1wlk06bB5imVEqnyGg9UByYD7/hepoQbPBhq13bTK1bAwIHB5seYXPbMMwVHXrznHjjllODykw6JNBue4/UKXOJZs+FdvfwyXHihmxaBTz+FQw8NNk/G5JpPPoHOnV2VF7hqr1deKTn3TVLZbHiCiJycgjyZLHT++XDSSW5a1XWbHTrpjTHOQw/BtGlF23bpUncTPvS9atIEWrUqOcEkGYkElOtwQWWLiPwpIutFxGrbc4QIPPmk69kU4Ouv4d//DjZPxmSbDh1c9yjJBpUNG6BrV/jtNzdfrRqsWQMdO6Y+j9kgkVZeVVW1lKpWUNVq3ny1TGTOZEajRnC3r5e0O++0Z1OM8evcGV59NbmgsnMnXHwxzJvn5kuXdj/gxo51+8tFibTyEhG5SERu9+YbiEjSvf6a7HbdddCmjZvessU9q2KjOxoTlmxQuf12ePPN8HzFiq6JcK4GE0isyutJXCeM3bz5DcDQ2MlNSVSmDDz3nPsVBfDRRwXHtDbGJB5UXnzRdaUSUrEivPVWbgcTSCygHKKqVwNbAFR1DZDyERxN8Nq0Kdj9w4AB4QewjDFOYUHl449dCT+kbNn8CCaQWEDZ7o0tr+AedASsMiRHDRoEBxzgpjdscK2+rFsWYwqKFVR+/BHOPDPcoqt0aXjjDejSJZh8ZloiAeUx3GiJe4rIvcAM4L74m5iSqkIFV/UVatL4/vvwvPWLYMwuIoPKH3+4BxVXex1TicDIkXDqqcHmM5MKfbARQEQOAI4FBJiiqiWyt2F7sDFx/fu7oYIBqlZ1zYkbNgw0S8ZkpWnTXFCpWzfcogtc1/R9+waXr1RK9Xgoi1R1qKo+UVKDiUnOvfeG+xhavx7+8Q9r9WVMNEcfDa1bFwwmt9+eO8EkGQkFFJN/KlVyAwCFeiSeMsWNSW+MKeiOO2Dy5PD8MccUfK4rn1hAMTEddhjceGN4/sYb7YFHY/yefdZ1shrSpo0rqRS1m5aSzgKKieuuu9wAQACbNsFFF1lfX8YATJwIffqE59u3hy++SP6J+lxiAcXEVb68a6lStqyb//JLd3/FmHw2a5brMfivv9z8vvvC1Knue1KUblpyRSABRUQGi8g8EZkjIpNEpG6MdEtE5Gsv3Szf8odFZJG3j3EislvGMp+H2rQpWKy/5x74/PPg8mNMkH780TUP3rTJzdeu7XqWqFo1nCZfg0pQJZSHVbWVN87KBGBQnLSdVbV1RJO1D4AWqtoK+A64JX1ZNQA33ACdOrnpv/5yVV/r1webJ2My7ddf4fjjw70HV6niAkadOrumzcegEkhAUVV/9/eV8Z7CT2L7Saq6w5v9HKifqryZ6EqXdv0TVfP6mf7xR7j66mDzZEwm/fmnGzvof/9z82XLwnvvQbNmsbfJt6AS2D0UEblXRJYB3YldQlFgkojMFpHeMdL0BN6N8z69RWSWiMxatWpV8TKd5xo2hP/8Jzw/cqR7GZPrtmxxXap89ZWbD3VDn8i4JvkUVNIWUERksojMj/I6HUBVB6pqA2AUEOsRoI6q2hY4CbhaRDpFvMdAYIe3j6hUdZiqtlfV9rVq1UrJZ8tny5e7In/IlVcWvSnxtGluJDxjstmOHdCtm7vpHvLMM27grETlS1BJW0BR1S6q2iLKa3xE0tHA2TH2scL7+xuuP7G/x2ERkR7AqUB3TaT/GJMSHTrA7NlQ36tk3LgRLrgAtm5Nbj+h7io6dEh9Ho1JFVW44go3jknIPfe4niOSlQ9BJahWXk19s12BRVHSVBaRqqFp4Hhgvjd/InAT0FVVN6U/xyakc2d47TXXE3GZMm7Zf/9b8AHIwoSCyauv5keX3qZkUnVDOPg7R+3fH269tej7DAWVmTOLn79slFDnkCl/U5HXgf1x3eAvBfqo6s9e8+FnVfVkEWmMK5UAlAFGq+q93vY/AOUBr19PPlfVPhTCOodMnWnT4LTTXAkl5NVXXdv8wrazYGJKgsGD3XAOIT16uOBSKg+f3ku0c8hAAkpQLKCk1tSprtXLtm1uvmpV98DXfvtFT2/BxJQUQ4a40kjI6ae7m/ChUnm+SWlvw8ZEc8wx7ksW+sW2fr0roWzevGtaCyampHj22YLBpEsXePnl/A0mybCAYorltNMKNiWeN8/1b+Qv+FowMSXFSy+5UUpDOnaEN990A8+ZwllAMcXWuzdcf314/sUXYehQN23BxJQUr7zi7pOEfgy1awfvvAOVKwebr5LECnEmJR55BBYuhHe9R0z793ddtNxzjwUTk/3eeAO6dw8PIteypRv+unr1YPNV0lgJxaSEiPtShm7I79jhgsrQoRZMTHZ78004//xwz8EHHugGzKpRI9BslUgWUEzKVKgAH3wAFSu6eVV4+OHoN+mNyQbjxrmGJDu8ngH328+NTrrnnsHmq6SygGJS6scfoVw5V2IB14y4Z8+CN+mNyQbjxrn7e6Fg0rSpawq/117B5qsks4BiUiZ0A37cOHj88fDyl1+G++8PLl/GRBo7dtdgMm0a1KsXbL5KOgsoJiUiW3NddVXB4VEHDnT3WIwJ2qhR7p6JBZPUs4Biii1a02AReOwxOProcLoLL3RDCBsTlOefh4svDrfm2n9/CyapZAHFFEu850zKlnVVC/vu6+a3bYMTToAlSzKeTWN44gnXS3Dofl6LFvDhhxZMUskCiimyRB5arFEDJk6EPfZw82vXwlFHub/GZIIq3HsvXHNNeFmbNu78rV07uHzlIgsopkiSeQK+aVPX1r9cOTf/008uqGzZkvZsmjynCjfdBLfdFl522GGuaXDNmsHlK1dZQDFJK0p3KkceCS+8EJ6fN8+N/Bh6mMyYVNuxA3r1cs9ChXTpApMmwe67B5evXGYBxSSlOH1zdesGDz4Ynv/4YzjrLHtGxYQ99FBqRjPcvNmVgv2DY51xBrz9NlSpUvz9m+gsoJikzJxZvL65brwR+vULz7/1Ftx1V0qyZnJAhw7FHyJ37Vo45BD49NPwsh493Eij1mtwellAMUkZMKB4fXOJwL/+5ZoQh9x1F/z738XPmyn5ijvu+k8/uRvuX38dXjZggKtutfFM0s8Cism4UqVg+HDXhDjkn/+EZ54JLEsmixQ1qMydC23bFmyW/vDDrpo11BWQSS8LKCYQ5cq5J+ePPDK87Ior3FPMxiQbVD74wLXeWr3azZct6wbLuuGG9ObTFGQBxQSmUiWYMAHaeyNVq8Ill8CYMcHmy2SHRIPK00/DiSeGe7WuVg3ee8+Nb2IyywKKCVToy9+ihZvfuRMuusiNnmdMvKDy11+uqrRPn3BXKvXqwYwZcMwxmc+rCSigiMhgEZknInNEZJKI1I2RbomIfO2lm5Xs9qZkqFHDPWjWvLmb37nT/bq0oGIgelD580/XDNjfmKNtW9dXXMuWgWTTEFwJ5WFVbaWqrYEJwKA4aTuramtVbV/E7U0JsOeebiyKAw9083/95Z5bGT480GyZLOEPKi+95O6XTJgQXn/GGfDRR1DXfloGKpCAoqp/+mYrA0k92lbc7U12igwqO3fCZZe5YYSN6dwZbr7Z3WdbsCC8fMAAeP11qFw5uLwZJ7B7KCJyr4gsA7oTu4ShwCQRmS0ivYuwPSLSW0RmicisVatWpSr7Jk1q14bp06F16/Cyvn3hgQfsifp8FhpOesCA8HlQujSMHOmaBZeyu8FZIW3/BhGZLCLzo7xOB1DVgaraABgF9I2xm46q2hY4CbhaRDqFViS4Pao6TFXbq2r7WrVqpezzmfSpVcvVlR96aHjZLbfA9deHb76a/LFhgxsQa8CA8P+/ShWoWtW6ns82aQsoqtpFVVtEeY2PSDoaODvGPlZ4f38DxgEHR0kWc3tTcu22m3u2wP9U/pAhrgXYtm1B5cpk2oIFcPDBrtuUkObN4bvv3HNMxe2mxaRWUK28mvpmuwKLoqSpLCJVQ9PA8cD8RLc3JV+VKm4slXPOCS8bMwZOOcXGU8kHI0e6vr0WLgwvO+MM+O9/oU6d4nfTYlIvqJrHB7zqr3m4QHEdgIjUFZGJXprawAwRmQt8Cbyjqu/F297kngoV4OWX3Rj1IZMnQ8eOsHhxcPky6bNxo+t2/pJLYNOm8PKbb4Zx48Lj6oAFlWwjmkd3Otu3b6+zZs0qPKHJOqFR926/PbysVi0YP941ITW5Ye5cuOACWOSrcyhdGoYNg549Y29XnGEVTOFEZHbEoxtRWdsIUyKIuFH3Ro0K/0JdtcpdPIYPT904GuD289BDqdmXSczOnfDYY67beX8wKV/eDXEQL5iAlVSyhQUUU6J06+aeValRw81v3eqeVfn889RcTEK/dDt0KH5eTWJ+/tn1xXXdde7/CS6QhO6hnXxyYvuxoBI8CyimxOnYEb74Itz/F7i69Xr13A38ol5MrNoks1Td/bGWLV2LvpAmTVzHoW+9lXyfXBZUgmUBxZRITZrAZ5+5IYRD5s51F6kzz0z+YmLBJLN++QXOPtsNtLZmjVsm4p43WbfOPfle1P+DBZXgWEAxJVaVKu75hMGDwwMorVnjOg489VTXGiwRFkwyR9X1xdW8uStVhjRs6HpIaNs2Nf+HUFCZObN4+zHJsVZeJidMnuzur/h71ylbFkaMKDjccCQLJpnzv//BlVfCpEkFl19+OTzyiBvKwGQna+Vl8kqXLjBnDnTqFF62fbvrBn/gwOj9gFkwyYxt21x/Wy1aFAwmDRu6eyfDhlkwyRUWUEzOqFvXjaty553hzgJV4b774Kij4Ndfw2nzPZhkqpn15Mlw0EHuocTQiIoicO218PXX7oeAyR0WUExOKVMG7rgDPv4Y9tknvPzjj6FpUxg92jU7zudgAq5ZdDqbWf/vf67F3XHHFXyu5KCDXBPv//s/17mjyTGqmjevdu3aqckf69apXn65qiunhF9ly6q++GLQuQve1KmqNWu6v6nafs0a1RtvVC1XruAxr1ZN9dFHVbdtS0XOTaYBszSBa6yVUEzOqlbN1c9PmgR77x1evn27uxF8553haph8VJzmtZFVhlu2uOF4993XjVvi7xH64ovh22+hXz/XUMLkLgsoJucddxw8+aTraDJk61a46y7Yf3/Xg3EeNXYsoChBxR9MjjgCnn3WVSf+85+wenU43WGHuWeFXnwR9torPfk32cUCisl506bBpZe6bjxmz3ZBJGTZMtfc+PDD4cMPA8tioJIJKqFgMmoUfP897LefK+0tXx5O06iRewL+k08KDpJm8kAi9WK58rJ7KPknWj3/jh2q11+vKrLr/ZXjjlP94ovUvf+DDxb9HkWkqVPd/tKlsHsqU6eq1qihetVVqg0a7HrsatdWffxx1S1b0pdHEwwSvIcS+EU+ky8LKPmlsAvkW2+pVqyoWqbMrhfHE05Q/fBD1Z0705uHTO+nqO/zyivuWFWuvOuxqlFD9b77VNevT2/eTHAsoFhAyWuJXoCnTlXdYw/Vk05SLVVq14tlx46qr7+uun17+vOSru2L+n5TprigetRRux4XUK1Vy5WYLJDkPgsoFlDyVrIX4FD64cNVL7ggelXY3nu7i+evv2YmT8XdrjhWrlTt3Vu1dOnogaRJE9Unn1TduDFzeTLBsoBiASUvpeLC/d13qr16uedVIi+mZcqonnWW6jvvJF9qKWqgy0QwWbdOdcQI1RNPjB1Ijj7aldZ27Eh/fkx2sYBiASXvpLpqafly1YED3bJoF9iaNVX79FGdNi3x4JJMVVy6g8lvv6k+/7xq166qFSpE/4zlyrl1zz2XvnyY7GcBxQJKXknnze/Nm1VfeEH1sMOiX3TB3Yfp3l119Gh3oS5OXtMVTLZtU/3kE9VBg1QPPTT6PSN/Seymm1Q3bAim2s1kl6wOKMBgYB4wB5gE1I2RbgnwtZdulw8E3AAoUDOR97WAkrsy1Tx3wQLVG25QrVs39sUYVFu0UL3mGtUxY1R//HHX1mKxLtKpvHj/+qvqxImqd9yheswxqpUqxc9zq1auq5o99khvvkzJk2hACWQ8FBGppqp/etPXAgeqap8o6ZYA7VX19yjrGgDPAgcA7aKliWTjoZhU2bkTZsyAV15xA0WtXBk/fY0armPEAw90g0s1beq26d8/3H1JUXpA3rbNPVS4dCn8+CMsXAgLFriefH/+Of62Iu6BzjPOgNNPd/uJ9/753kNzPkt0PJTAB9gSkVuAvVX1yijrlhA7oIzFlXTGx0oTyQKKSQdV+OoreOcdeO89+PJL2LEj8e1FXMBZt8712tuwIVSuXLDfq23bYNMm91q71g0ktmoV/P57ct3GNGzouow//ng49lj3vpB4sLCgkp+yPqCIyL3AJcA6oLOqroqSZjGwBlet9bSqDvOWdwWOVdXr4gWdSBZQTCZs3Oi6HfnoIxdcZs50QSDTKlaE1q2hXTtXEjnySKhff9d0yQYJCyr5J/CAIiKTgWhdwg1U1fG+dLcAFVT1jij7qKuqK0RkT+AD4BpgFjANOF5V1xUWUESkN9AbYO+99263dOnSYn4yY5Kj6sYH+eYbVx21YAEsXgxLlhTsA6soRKBOHVfyaNQImjVzrwMPdP1slSkTf/uiBgcLKvkl8ICSKBFpCLyjqi0KSXcnsAF4H5gCbPJW1QdWAAer6i/x9mElFJNNQhflJ590F/8PPoDBg+Hqq12A+OuvcNoyZVw1WKVKbmCqWrXcq2ZNKFeueO9f1KBgQSV/JBpQCvn9kh4i0lRVv/dmuwKLoqSpDJRS1fXe9PHA3ar6NbCnL90SEqzyMiZbRLsYH3SQq57KxEU6FcHA30uxBRUDwXVf/4CIzBeRebhAcR24Ki4RmeilqQ3MEJG5wJe4Usx7wWTXmNSJdzEvzqBXyZg5MzVBIJTfmTNTky9TsgVe5ZVJVuVlgmatqUxJlGiVlw2wZUyGJBMkMlVSMSaVLKAYkwFFKXFYUDEljQUUY9KsONVXFlRMSWIBxZg0SnVrKgsqJptZQDEmjaw1lcknedXKS0RWAf5H5WsC+f78ih0DOwb5/vnBjgHEPwYNVbVWYTvIq4ASSURmJdIULpfZMbBjkO+fH+wYQGqOgVV5GWOMSQkLKMYYY1Ii3wPKsKAzkAXsGNgxyPfPD3YMIAXHIK/voRhjjEmdfC+hGGOMSRELKMYYY1IiJwOKiDwvIr+JyHzfsj1E5AMR+d77u3uMbZeIyNciMkdESmzXxDGOwbki8o2I7BSRmM0DReREEflWRH4QkZszk+PUKubnz+Vz4GERWSQi80RknIjsFmPbEn8OQLGPQS6fB4O9zz9HRCaJSN0Y2yZ3Hqhqzr2ATkBbYL5v2UPAzd70zcCDMbZdAtQM+jOk6Rg0A/YHpuMGJYu2XWngR6AxUA6YCxwY9OfJ1OfPg3PgeKCMN/1gtO9BrpwDxTkGeXAeVPNNXws8lYrzICdLKKr6EfBHxOLTgRHe9AjgjEzmKdOiHQNVXaiq3xay6cHAD6r6P1XdBryMO3YlSjE+f86IcQwmqeoOb/Zz3BDakXLiHIBiHYOcEeMY/OmbrQxEa52V9HmQkwElhtqquhLA+7tnjHQKTBKR2SLSO2O5yx71gGW++eXesnySL+dAT+DdKMvz6RyIdQwgx88DEblXRJYB3YFBUZIkfR7kU0BJVEdVbQucBFwtIp2CzlCGSZRl+da2POfPAREZCOwARkVbHWVZzp0DhRwDyPHzQFUHqmoD3OfvGyVJ0udBPgWUX0WkDoD397doiVR1hff3N2AcrtiXT5YDDXzz9YEVAeUlELl+DohID+BUoLt6leURcv4cSOAY5Px54DMaODvK8qTPg3wKKG8BPbzpHsD4yAQiUllEqoamcTfv5kemy3EzgaYiso+IlAMuwB27vJDr54CInAjcBHRV1U0xkuX0OZDIMciD86Cpb7YrsChKsuTPg6BbIKSpVcMYYCWwHRdl/wHUAKYA33t/9/DS1gUmetONcS0Z5gLfAAOD/iwpPgZnetNbgV+B9yOPgTd/MvAdroVHiTwGRf38eXAO/ICrF5/jvZ7K1XOgOMcgD86D13EBch7wNlAvFeeBdb1ijDEmJfKpyssYY0waWUAxxhiTEhZQjDHGpIQFFGOMMSlhAcUYY0xKWEAxxhiTEhZQjDHGpIQFFGNyiIg8LiL/FZEO3nwzEXlKRMaKyJVB58/kNgsoxuQIr4uQPYErcP1Uoa7L/j7AeUDMQcWMSQULKMb4iMh0ETkhYlk/EXkyzjYb0p+zXd6zooh8KCKlQ8tUdSNQBzeA2GO+tF2BGbguhxCRciLykYiUyWyuTa6zgGJMQWNwneD5XeAtzyY9gTdU9a/QAhGpAVQC1gN/L1fVt1T1cNy4F6gbLGkKcH5Gc2xyngUUYwoaC5wqIuUBRKQRrsO8GSJyvYjM9179IjcUkUYR43bfICJ3+tYtEpFnve1HiUgXEflERL4XkYO9dBeJyJfeWN9P+0sgEbqza4/ZtwGP4DozPNDb39Ei8piIPA1M9KV909uHMSljAcUYH1VdDXwJnOgtugB4BTcm92XAIcChwOUi0ibJ3e8L/B/QCjgA6AYcAdwA3CoizXClho6q2hpXytjlou91Jd5YVZf4ljUCDvfyuhBo7n2e6ap6rapeoapDfbuZD3RIMv/GxGUBxZhd+au9QtVdRwDjVHWjqm4A3gCOTHK/i1X1a1XdiStFTFHX3ffXQCPgWKAdMFNE5njzjaPspyawNmLZPcDd3v7+DiixeFVl20JjfhiTCnZTzphdvQn8W0TaAhVV9b8JDv+6g4I/0ipErN/qm97pm9+J+y4KMEJVbynkfTb79y0irYGzgCNEZKi37usE8lse2JJAOmMSYiUUYyJ4JZDpwPOEb8Z/BJwhIpW85rlnAh9HbPorsKeI1PDuwZya5FtPAc4RkT0BRGQPEWkYJX9rgNIiEgoqDwKnqWojVW0EHEQhJRTvBv4qVd2eZB6NiclKKMZENwZXrXUBgFdKGY67vwLwrKp+5d9AVbeLyN3AF8Biog+rGpOqLhCR24BJIlIKN8Le1cDSKMkn4UokO4HKqjrFt59fvSFs91DVP2K8XWcK3qQ3pthsxEZjSiCvQcD1qnpxEbd/A7hFVb9Nbc5MPrMqL2NKIK90NC1Os+KYvFZib1owMalmJRRjjDEpYSUUY4wxKWEBxRhjTEpYQDHGGJMSFlCMMcakhAUUY4wxKWEBxRhjTEr8P2AU20bL8TzLAAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2172,13 +1946,13 @@
{
"cell_type": "code",
"execution_count": 48,
- "id": "identical-stuart",
+ "id": "dependent-singapore",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(11.808803921590759, 141.83475481259447)"
+ "(11.92097747703976, 148.2870088940015)"
]
},
"execution_count": 48,
@@ -2193,13 +1967,13 @@
{
"cell_type": "code",
"execution_count": 49,
- "id": "immune-resident",
+ "id": "elegant-bryan",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "3.614836295562474"
+ "3.626246196237334"
]
},
"execution_count": 49,
@@ -2214,7 +1988,7 @@
{
"cell_type": "code",
"execution_count": 50,
- "id": "sunrise-reach",
+ "id": "analyzed-camel",
"metadata": {},
"outputs": [],
"source": [
@@ -2234,37 +2008,31 @@
{
"cell_type": "code",
"execution_count": 51,
- "id": "certified-acting",
+ "id": "immediate-modem",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 5/5 [00:00<00:00, 351.08it/s]\n",
- " 0%| | 0/5 [00:00<?, ?it/s]"
+ "100%|██████████| 3/3 [00:00<00:00, 916.85it/s]\n",
+ "100%|██████████| 3/3 [00:00<00:00, 94.08it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job table_murn was saved and received the ID: 4370\n"
+ "The job table_murn was saved and received the ID: 51\n",
+ "CPU times: user 127 ms, sys: 9.39 ms, total: 136 ms\n",
+ "Wall time: 157 ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 5/5 [00:00<00:00, 24.24it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 406 ms, sys: 156 ms, total: 562 ms\n",
- "Wall time: 608 ms\n"
+ "\n"
]
},
{
@@ -2297,38 +2065,24 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>3723</td>\n",
- " <td>3.637366</td>\n",
- " <td>12.030984</td>\n",
- " <td>152.193869</td>\n",
+ " <td>21</td>\n",
+ " <td>3.629850</td>\n",
+ " <td>11.956550</td>\n",
+ " <td>146.334721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>3733</td>\n",
- " <td>3.614933</td>\n",
- " <td>11.809748</td>\n",
- " <td>135.833772</td>\n",
+ " <td>31</td>\n",
+ " <td>3.630147</td>\n",
+ " <td>11.959492</td>\n",
+ " <td>143.972401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>3743</td>\n",
- " <td>3.614836</td>\n",
- " <td>11.808804</td>\n",
- " <td>141.834755</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4308</td>\n",
- " <td>3.614933</td>\n",
- " <td>11.809748</td>\n",
- " <td>135.833772</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>4318</td>\n",
- " <td>3.614836</td>\n",
- " <td>11.808804</td>\n",
- " <td>141.834755</td>\n",
+ " <td>41</td>\n",
+ " <td>3.626246</td>\n",
+ " <td>11.920977</td>\n",
+ " <td>148.287009</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -2336,11 +2090,9 @@
],
"text/plain": [
" job_id a eq_vol eq_bm\n",
- "0 3723 3.637366 12.030984 152.193869\n",
- "1 3733 3.614933 11.809748 135.833772\n",
- "2 3743 3.614836 11.808804 141.834755\n",
- "3 4308 3.614933 11.809748 135.833772\n",
- "4 4318 3.614836 11.808804 141.834755"
+ "0 21 3.629850 11.956550 146.334721\n",
+ "1 31 3.630147 11.959492 143.972401\n",
+ "2 41 3.626246 11.920977 148.287009"
]
},
"execution_count": 51,
@@ -2363,7 +2115,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "reasonable-liability",
+ "id": "persistent-williams",
"metadata": {},
"outputs": [],
"source": []
@@ -2385,7 +2137,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.8"
+ "version": "3.8.6"
},
"toc-autonumbering": false,
"toc-showcode": false,
diff --git a/day_1/ex_02_creating_structure_databases.ipynb b/day_1/ex_02_creating_structure_databases.ipynb
index e0164839de3f9f23625d31490d30759b5ea7a198..4bf2ef7c69bac30b6ed850a1398c67aa2c242c1e 100644
--- a/day_1/ex_02_creating_structure_databases.ipynb
+++ b/day_1/ex_02_creating_structure_databases.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "appointed-stylus",
+ "id": "orange-treaty",
"metadata": {},
"source": [
"# [**Workflows for atomistic simulations**](http://potentials.rub.de/) "
@@ -10,7 +10,7 @@
},
{
"cell_type": "markdown",
- "id": "straight-bicycle",
+ "id": "lasting-stick",
"metadata": {},
"source": [
"## **Day 1 - Atomistic simulations with [pyiron](https://pyiron.org)**\n",
@@ -29,7 +29,7 @@
},
{
"cell_type": "markdown",
- "id": "durable-leone",
+ "id": "sharing-maldives",
"metadata": {},
"source": [
"## **Importing necessary modules and creating a project**\n",
@@ -40,7 +40,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "novel-wisconsin",
+ "id": "serial-slovak",
"metadata": {},
"outputs": [],
"source": [
@@ -52,7 +52,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "sitting-religious",
+ "id": "departmental-certification",
"metadata": {},
"outputs": [],
"source": [
@@ -62,7 +62,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "technical-newport",
+ "id": "french-darwin",
"metadata": {},
"outputs": [],
"source": [
@@ -71,7 +71,7 @@
},
{
"cell_type": "markdown",
- "id": "based-kentucky",
+ "id": "electoral-norman",
"metadata": {},
"source": [
"## Creating a structure \"container\" from the data\n",
@@ -83,8 +83,8 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "id": "attached-germany",
+ "execution_count": 4,
+ "id": "demanding-proxy",
"metadata": {},
"outputs": [],
"source": [
@@ -94,8 +94,8 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "id": "filled-natural",
+ "execution_count": 5,
+ "id": "alert-undergraduate",
"metadata": {},
"outputs": [],
"source": [
@@ -105,7 +105,7 @@
},
{
"cell_type": "markdown",
- "id": "married-storm",
+ "id": "comprehensive-sudan",
"metadata": {},
"source": [
"## **Add structures from the E-V curves**\n",
@@ -115,8 +115,8 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "id": "mediterranean-upset",
+ "execution_count": 6,
+ "id": "compatible-sight",
"metadata": {},
"outputs": [],
"source": [
@@ -127,7 +127,7 @@
},
{
"cell_type": "markdown",
- "id": "acquired-missile",
+ "id": "illegal-capture",
"metadata": {},
"source": [
"We can obtain this data as a `pandas` table"
@@ -135,8 +135,8 @@
},
{
"cell_type": "code",
- "execution_count": 18,
- "id": "greatest-person",
+ "execution_count": 7,
+ "id": "impressive-basin",
"metadata": {},
"outputs": [
{
@@ -170,326 +170,6 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
- " <td>-3.142019</td>\n",
- " <td>[[1.1869253621046177e-16, -1.7429070520896771e-16, -1.397277865277868e-16]]</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
- " <td>-3.338596</td>\n",
- " <td>[[-1.92404082484227e-16, 4.231084758750405e-17, 3.6193775346684653e-17]]</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
- " <td>-3.416929</td>\n",
- " <td>[[-2.9113397169423695e-17, 7.54965057835309e-17, -3.624419431643654e-17]]</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
- " <td>-3.409602</td>\n",
- " <td>[[3.771496125435321e-17, 3.412312546579927e-17, -2.4310047599025677e-17]]</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
- " <td>-3.330215</td>\n",
- " <td>[[-2.0545362501508919e-16, -3.5486130576273854e-17, 3.5486130576273854e-17]]</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
- " <td>-3.195118</td>\n",
- " <td>[[1.6101257219667079e-16, -4.2948421129906387e-17, 4.2948421129906387e-17]]</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
- " <td>-3.035358</td>\n",
- " <td>[[-5.946777565406637e-17, -1.0605082175909553e-16, -1.2946304704347008e-16]]</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0), Atom('Cu', [0.0, 1.8049999999995918, 1.804999999999592], index=1), Atom('Cu', [1.8049999999995918, 1.1052437362302365e-16, 1.804999999999592], index=2), Atom...</td>\n",
- " <td>-347.182406</td>\n",
- " <td>[[-1.2656542480726799e-14, -1.46965772884755e-14, -1.61017033040167e-14], [-1.3905543383430098e-14, 4.5310977192514186e-15, 4.8333732849403796e-15], [4.9682480351975795e-15, -1.4072076837123899e-1...</td>\n",
- " <td>108.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.140426153531212, 11.00934611760493, 10.968207696001379], index=0), Atom('Cu', [10.983357200359302, 1.779939365335074, 1.7146804782560905], index=1), Atom('Cu', [2.1228644677344763, ...</td>\n",
- " <td>-348.253665</td>\n",
- " <td>[[-0.21910202935187897, -0.37573419410584397, 0.43392575377979187], [0.16208168404695897, -0.00671505675904709, 1.03458554920361], [-1.2001630139266497, -0.40207322348963503, -0.45620473735655703]...</td>\n",
- " <td>108.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.1407579358923329, 11.020287239626356, 10.855878337455094], index=0), Atom('Cu', [0.29542104007972325, 1.6514729828183248, 1.7760939949715002], index=1), Atom('Cu', [1.99578258187561...</td>\n",
- " <td>-345.424528</td>\n",
- " <td>[[-0.023031834879881696, 0.042841438691562095, 0.5899774836434479], [-0.5418151518759569, 0.6754733604036028, -0.5582999589284809], [-0.6011411771360858, -0.355590065329821, -0.0035901986306415582...</td>\n",
- " <td>108.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.04675277832683521, 10.951786003502875, 0.20014601215416555], index=0), Atom('Cu', [0.08453269827616121, 1.807218974303731, 1.7664733621606055], index=1), Atom('Cu', [1.9597357123378...</td>\n",
- " <td>-346.758349</td>\n",
- " <td>[[-0.32237334386615796, 0.43406651671724894, -0.5886238546572939], [-0.6295919499998729, 0.07530471384300086, -0.12687342568230403], [-0.06506588733991228, 0.8782024477953109, -0.12243680387297393...</td>\n",
- " <td>108.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.991332780892245, 11.027168829788469, 0.17044757336885022], index=0), Atom('Cu', [10.869778028731718, 1.9053524080340238, 1.7569642307109068], index=1), Atom('Cu', [1.89027128290555...</td>\n",
- " <td>-344.603627</td>\n",
- " <td>[[0.45078377546738896, -0.7167806257867769, -0.320969733282763], [0.5707773027838049, -0.5494069530705199, 0.510256621543289], [-0.36439749359274193, 0.17709586752044496, 0.23127352998569298], [0....</td>\n",
- " <td>108.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.958070767872973, 0.05270018288525903, 11.015795828313184], index=0), Atom('Cu', [0.04540970504125744, 2.0780519854689388, 1.8353871076994486], index=1), Atom('Cu', [1.8285627799497...</td>\n",
- " <td>-346.849801</td>\n",
- " <td>[[-0.38409855462219894, 0.12077975249818296, 0.024465771201292483], [-0.6560764999451358, -1.05845756801878, -0.658182913095082], [-0.5103420261962529, -0.35656271154938, 0.7090309519821769], [-0....</td>\n",
- " <td>108.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.903697402270481, 10.843249424147054, 0.09574490333935876], index=0), Atom('Cu', [10.594918916668115, 1.7187193807013557, 1.957301113531466], index=1), Atom('Cu', [1.676074002401865...</td>\n",
- " <td>-345.015235</td>\n",
- " <td>[[0.22044015995668098, -0.2607278388818459, -0.3855032322190499], [0.7754186544660099, -0.04652340102386694, -0.26232164996063695], [0.43147792874898694, -0.9840256256911009, 0.34987911571105595],...</td>\n",
- " <td>108.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.25841198959969514, 0.07063449316617948, 10.912283030401635], index=0), Atom('Cu', [0.19649020014428978, 1.7891621064498946, 1.803181486480956], index=1), Atom('Cu', [1.7493171890008...</td>\n",
- " <td>-346.569097</td>\n",
- " <td>[[-0.5020206209676749, 0.007307596544171969, 0.24586848661916993], [-1.1645196400331899, 0.1476285180684419, 0.6168850904586589], [0.7183114431792579, -0.5420093171036479, -0.06687387962480258], [...</td>\n",
- " <td>108.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.004994793789590358, 10.985055637636528, 10.904222762438215], index=0), Atom('Cu', [10.941729664004177, 1.8868614122932958, 1.8147512571785023], index=1), Atom('Cu', [1.8438031255437...</td>\n",
- " <td>-344.892954</td>\n",
- " <td>[[0.04806202889719739, 0.48969724353819394, 1.29371057615331], [0.5538169933185199, -0.7855310261714289, -0.033081946412792815], [0.34832740609982993, 0.9937361742308158, 0.30650548838004904], [-0...</td>\n",
- " <td>108.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.03645923830333788, 10.856010693932728, 10.970572990196315], index=0), Atom('Cu', [10.913522535008392, 1.8161914381204696, 1.5447581077411967], index=1), Atom('Cu', [1.77583933019795...</td>\n",
- " <td>-347.628843</td>\n",
- " <td>[[0.24033044872062098, 1.5305792677179897, -0.6791236163119347], [0.05903491332573559, 0.148151392595253, 0.542468964409148], [0.07921843405670739, 0.145966157214324, 0.7342269238916159], [0.77503...</td>\n",
- " <td>108.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.009767181825404037, 0.0071865231528328395, 11.0089023992238], index=0), Atom('Cu', [10.89374596605574, 1.9969194212313586, 1.595578837066423], index=1), Atom('Cu', [2.05442192913884...</td>\n",
- " <td>-345.776979</td>\n",
- " <td>[[-0.13926025211222698, -0.0920021629424733, 0.21001691285267599], [0.6767944835284789, -1.1148612203782198, 1.7165284718417098], [-0.7950973845960859, 0.2356681337270629, 0.13328556064538893], [0...</td>\n",
- " <td>108.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 1.8049999999995918, 1.804999999999592], index=0), Atom('Cu', [1.8049999999995918, 1.1052437362302365e-16, 1.804999999999592], index=1), Atom('Cu', [1.8049999999995918, 1.80499999...</td>\n",
- " <td>-342.906251</td>\n",
- " <td>[[-1.3933298959045699e-14, -0.10827842091784899, -0.108278420917849], [-0.10827842091784899, -1.3877479054990188e-14, -0.108278420917849], [-0.10827842091784899, -0.108278420917849, -1.38424758326...</td>\n",
- " <td>107.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.10390109091492504, 1.9580560553035629, 1.779188560767685], index=0), Atom('Cu', [1.3868099266682106, 10.93717630776342, 1.601790948781396], index=1), Atom('Cu', [1.7183987889409966,...</td>\n",
- " <td>-343.569002</td>\n",
- " <td>[[-0.45079778130218695, 0.19236747741170795, 0.23614131969524693], [1.2984317307784499, 0.04751568933291938, 0.005443425577977482], [0.48875760189522893, -0.5765163470127929, -0.5531364786691719],...</td>\n",
- " <td>107.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.2223234793002756, 1.6999920633404135, 1.7623980235259114], index=0), Atom('Cu', [2.122882660879041, 0.18624238423025943, 2.030808683988878], index=1), Atom('Cu', [1.7334689484700414...</td>\n",
- " <td>-343.317892</td>\n",
- " <td>[[-0.6920262907044069, 0.3986975029533409, 0.10738641941643497], [-1.2097205794553298, -2.21423543020926, -0.8363385508172672], [-0.36839592956658596, 0.35919893904268096, 0.09738195090664269], [2...</td>\n",
- " <td>107.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.839434974930338, 1.9008464900018593, 1.6867963294684278], index=0), Atom('Cu', [1.5876598581358559, 0.15447557118789498, 1.715299994817798], index=1), Atom('Cu', [2.148606011735498...</td>\n",
- " <td>-341.057270</td>\n",
- " <td>[[0.5607305368018309, 0.07454461225822963, 0.17092757180402804], [0.7460358156721709, -0.9693709228665989, -0.357775642488428], [-1.4982109077380097, 1.0695224870660298, 0.17016025464137993], [1.1...</td>\n",
- " <td>107.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.827044041159013, 1.814885520198166, 2.1241909251750846], index=0), Atom('Cu', [1.6679142700199647, 0.1106240807045643, 1.629401459425781], index=1), Atom('Cu', [1.29418686613977, 2...</td>\n",
- " <td>-342.067006</td>\n",
- " <td>[[0.7768634672312369, 0.615614277472231, -0.6520778674544528], [0.5196497874591819, -0.7614150189249069, 0.33452284258026094], [1.0185593798470798, 0.11349094338622906, -1.1138567887728799], [0.39...</td>\n",
- " <td>107.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.18579648605077317, 1.6025971043596021, 1.8725196193757527], index=0), Atom('Cu', [2.0480835165906024, 0.055238510607153124, 2.258841094259334], index=1), Atom('Cu', [1.8423446841272...</td>\n",
- " <td>-342.528496</td>\n",
- " <td>[[-1.2511641505681497, 0.045660252990561916, 0.39426999337691987], [-0.4923847861277789, -0.676187288680257, -1.3802167704923], [-0.12072100498159098, 0.5666924914439229, -0.8825937576849239], [0....</td>\n",
- " <td>107.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.902653792490328, 1.9016481867720487, 1.712386801004505], index=0), Atom('Cu', [1.6843656295425262, 0.20454959542790652, 1.4940727709027473], index=1), Atom('Cu', [1.628967729295861...</td>\n",
- " <td>-342.900954</td>\n",
- " <td>[[0.5080309295986539, -0.4875222169174689, 0.46898176405272696], [0.11031309252296198, -0.7243436014688799, 1.2808414836059598], [0.6855285827079979, 0.576817447055947, 0.31599931473888204], [0.50...</td>\n",
- " <td>107.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.2202511036922136, 1.5872348575607442, 1.7623998700282655], index=0), Atom('Cu', [1.8386689358546338, 10.988833155546024, 1.8571406735818983], index=1), Atom('Cu', [1.976390891645592...</td>\n",
- " <td>-340.983380</td>\n",
- " <td>[[-0.6923371397061229, 0.9615117892959398, 0.396261839433264], [0.18083100499512797, -0.47103552335731996, 0.5072798013711759], [-0.22735248831930896, 0.37011177620569996, -1.1740345740817597], [0...</td>\n",
- " <td>107.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.746144724861097, 1.6561216261540705, 1.6676063346982197], index=0), Atom('Cu', [1.8697696006277247, 0.29566102043388925, 1.9071090181664805], index=1), Atom('Cu', [1.82013088394316...</td>\n",
- " <td>-342.479965</td>\n",
- " <td>[[0.46940172398488794, 0.557542533896084, 0.861383405261689], [-0.16043498489103097, -1.0997839627759998, -0.013974118313924476], [-0.13554483762266598, 0.6363641149062679, 0.153788969879775], [1....</td>\n",
- " <td>107.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.17063059290766686, 1.8534187978406447, 1.7423748775395635], index=0), Atom('Cu', [1.8595651370150335, 10.90572322929213, 1.810598025463111], index=1), Atom('Cu', [1.934651223968069,...</td>\n",
- " <td>-340.174298</td>\n",
- " <td>[[-1.1653987834207897, -0.07515193371140247, -0.13414295102360305], [0.17707911646472896, 0.37588362216028093, 0.10312993516925903], [0.6003632691780849, 0.3869695806628, 0.03431754153949555], [-0...</td>\n",
- " <td>107.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.866365842640102, 1.8491297818376022, 2.093244182315977], index=0), Atom('Cu', [1.7536272335648937, 10.898985481755824, 1.8241252890179567], index=1), Atom('Cu', [1.994555230114336,...</td>\n",
- " <td>-343.805462</td>\n",
- " <td>[[0.42819147658911194, -1.3404675072584398, -0.840042992949223], [0.47822853532413195, -0.07949121681390955, -0.36737406099153697], [-0.9172012989939929, 0.3239785862266829, -0.013009375610539234]...</td>\n",
- " <td>107.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 1.4737763285740602, 9.024277317236803e-17], index=0), Atom('Cu', [2.5526554800834376, 1.4737763285740604, 2.4654784132287465e-16], index=1), Atom('Cu', [5.105310960166875, 1.4737...</td>\n",
- " <td>-400.111422</td>\n",
- " <td>[[-8.770761894538741e-15, 1.15657750043852e-09, 0.109956784695642], [-1.0824674490095302e-15, 1.15657515335765e-09, 0.10995678469564], [2.4308896909297503e-15, 1.1565748914144101e-09, 0.1099567846...</td>\n",
- " <td>128.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.4126496185424086, 0.9553642998076769, 0.12158924761653131], index=0), Atom('Cu', [2.831118842513476, 1.0700158216349893, 25.916922304272767], index=1), Atom('Cu', [5.314789299948389...</td>\n",
- " <td>-399.591699</td>\n",
- " <td>[[-0.570999988178131, 1.09833300989089, -0.566585257198732], [-0.0492000252908114, 0.597416506778632, 0.966635869516025], [-0.0024866801464792, -0.0674126479000064, 0.2546322145358], [0.2289085885...</td>\n",
- " <td>128.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.151562776611806, 1.6587733567107183, 26.43035938894712], index=0), Atom('Cu', [2.5212828819853748, 1.9070842851152785, 26.36203940098048], index=1), Atom('Cu', [4.715098279443367, ...</td>\n",
- " <td>-398.422617</td>\n",
- " <td>[[-0.555658749547497, 0.42566314828364993, 0.466167261187443], [-1.39449792316952, -0.5017985009363372, 0.4456531170887249], [1.25410857633035, -0.33111996310077596, -0.24658722383829995], [0.3911...</td>\n",
- " <td>128.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>32</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.19573901040697333, 1.051460534941847, 26.530022925039237], index=0), Atom('Cu', [2.995726956631421, 1.1546751485293076, 26.840478692293868], index=1), Atom('Cu', [5.923947432763936,...</td>\n",
- " <td>-395.110340</td>\n",
- " <td>[[0.550884754551829, 0.33865983742272204, 0.12042096866985205], [0.189727589275692, -0.524367356684999, -0.17886090450862402], [-1.6758650723099, -0.31030261483377614, 0.6946509585213989], [0.4453...</td>\n",
- " <td>128.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>33</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [9.888379625890167, 1.4968159202602271, 26.58551059472236], index=0), Atom('Cu', [2.207544057015841, 1.290074245861123, 26.685466830386254], index=1), Atom('Cu', [4.939145903748478, 1....</td>\n",
- " <td>-396.833326</td>\n",
- " <td>[[0.235449441284142, 0.601163461325591, 0.04737458109310825], [0.337344266123105, 0.66485729403382, 0.37768738132523805], [-0.171831038530166, -0.11286098093109101, -0.07415135874528352], [-0.1966...</td>\n",
- " <td>128.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>34</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.3429326290376805, 1.5170285327525175, 0.14896315086845416], index=0), Atom('Cu', [3.129345868860857, 1.495102964861245, 0.01607848650845961], index=1), Atom('Cu', [5.714332108258016...</td>\n",
- " <td>-400.444368</td>\n",
- " <td>[[1.15453322332565, 0.04603660924771737, -1.21282260887155], [-0.403390913673288, -0.18969536874868004, 0.030526533198225264], [-0.683024325114862, -0.967333273082708, 0.4114320467737619], [-0.867...</td>\n",
- " <td>128.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [9.95801241508363, 1.4359511579313753, 26.639342243648862], index=0), Atom('Cu', [2.2681416259098204, 1.7861270127574713, 0.0041936446239185], index=1), Atom('Cu', [5.074253116948648, ...</td>\n",
- " <td>-397.843043</td>\n",
- " <td>[[0.742004122721361, 0.22577315893502206, 0.12298868177971806], [0.771069504028052, -0.90014541931557, -0.660403448738781], [-0.462456173466872, 1.24796716628589, -0.4192010350460109], [0.08219110...</td>\n",
- " <td>128.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.225027496779227, 1.7848555655274232, 26.42388292769549], index=0), Atom('Cu', [2.4914869938632442, 1.861119175102061, 26.64826771541495], index=1), Atom('Cu', [4.968442272624181, 1...</td>\n",
- " <td>-399.776798</td>\n",
- " <td>[[0.552749731038993, -0.46087407707755496, 0.177277816519943], [0.340079509783844, 0.23574679643440802, 0.26784307872713603], [0.388298578408562, 0.331969932822417, 0.34399524632239104], [-0.59826...</td>\n",
- " <td>128.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>37</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.188116701703345, 1.3482529143763475, 26.353847647582224], index=0), Atom('Cu', [2.4559442807081986, 1.4757747059762696, 26.57379282025388], index=1), Atom('Cu', [5.077086561123585,...</td>\n",
- " <td>-399.168074</td>\n",
- " <td>[[0.570532228156976, 0.17193739345012202, 0.796311675035985], [0.67297011060888, -0.81773224395498, 0.207420342553107], [0.391385096561351, -0.862073619446019, 0.597813478897486], [-0.270452726779...</td>\n",
- " <td>128.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>38</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.165259029706933, 1.7122531598094173, 26.335638965295193], index=0), Atom('Cu', [2.3513330869200098, 1.5487334168655387, 26.34258116233103], index=1), Atom('Cu', [4.95573138146394, ...</td>\n",
- " <td>-396.984690</td>\n",
- " <td>[[-1.11093024791963, -0.6986058376048401, 0.44674121624886787], [0.0416051560041239, -0.0424634970331458, -0.0874515615419252], [-0.135122617155289, -0.778975629913174, -1.04402814519501], [0.8442...</td>\n",
- " <td>128.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>39</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.36893784059756035, 1.481831927531004, 26.53504700964859], index=0), Atom('Cu', [2.6965712181950883, 1.488812429574298, 26.730123522873537], index=1), Atom('Cu', [5.668661292604278, ...</td>\n",
- " <td>-399.581912</td>\n",
- " <td>[[-0.645092069902244, 0.04920122139904896, 0.21081031345366397], [1.14893686034324, -0.6114894792387109, -0.40665475671972495], [-0.287092609914331, 0.292443458702414, -0.801010316546939], [-0.184...</td>\n",
- " <td>128.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>40</th>\n",
" <td>job_a_3_4</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.142019</td>\n",
@@ -497,7 +177,7 @@
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>41</th>\n",
+ " <th>1</th>\n",
" <td>job_a_3_5</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.338596</td>\n",
@@ -505,7 +185,7 @@
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>42</th>\n",
+ " <th>2</th>\n",
" <td>job_a_3_6</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.416929</td>\n",
@@ -513,7 +193,7 @@
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>43</th>\n",
+ " <th>3</th>\n",
" <td>job_a_3_7</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.409602</td>\n",
@@ -521,7 +201,7 @@
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>44</th>\n",
+ " <th>4</th>\n",
" <td>job_a_3_8</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.330215</td>\n",
@@ -529,7 +209,7 @@
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>45</th>\n",
+ " <th>5</th>\n",
" <td>job_a_3_9</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.195118</td>\n",
@@ -537,7 +217,7 @@
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>46</th>\n",
+ " <th>6</th>\n",
" <td>job_a_4_0</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.035358</td>\n",
@@ -549,253 +229,35 @@
"</div>"
],
"text/plain": [
- " name \\\n",
- "0 None \n",
- "1 None \n",
- "2 None \n",
- "3 None \n",
- "4 None \n",
- "5 None \n",
- "6 None \n",
- "7 None \n",
- "8 None \n",
- "9 None \n",
- "10 None \n",
- "11 None \n",
- "12 None \n",
- "13 None \n",
- "14 None \n",
- "15 None \n",
- "16 None \n",
- "17 None \n",
- "18 None \n",
- "19 None \n",
- "20 None \n",
- "21 None \n",
- "22 None \n",
- "23 None \n",
- "24 None \n",
- "25 None \n",
- "26 None \n",
- "27 None \n",
- "28 None \n",
- "29 None \n",
- "30 None \n",
- "31 None \n",
- "32 None \n",
- "33 None \n",
- "34 None \n",
- "35 None \n",
- "36 None \n",
- "37 None \n",
- "38 None \n",
- "39 None \n",
- "40 job_a_3_4 \n",
- "41 job_a_3_5 \n",
- "42 job_a_3_6 \n",
- "43 job_a_3_7 \n",
- "44 job_a_3_8 \n",
- "45 job_a_3_9 \n",
- "46 job_a_4_0 \n",
- "\n",
- " atoms \\\n",
- "0 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "1 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "2 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "3 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "4 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "5 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "6 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "7 (Atom('Cu', [0.0, 0.0, 0.0], index=0), Atom('Cu', [0.0, 1.8049999999995918, 1.804999999999592], index=1), Atom('Cu', [1.8049999999995918, 1.1052437362302365e-16, 1.804999999999592], index=2), Atom... \n",
- "8 (Atom('Cu', [0.140426153531212, 11.00934611760493, 10.968207696001379], index=0), Atom('Cu', [10.983357200359302, 1.779939365335074, 1.7146804782560905], index=1), Atom('Cu', [2.1228644677344763, ... \n",
- "9 (Atom('Cu', [0.1407579358923329, 11.020287239626356, 10.855878337455094], index=0), Atom('Cu', [0.29542104007972325, 1.6514729828183248, 1.7760939949715002], index=1), Atom('Cu', [1.99578258187561... \n",
- "10 (Atom('Cu', [0.04675277832683521, 10.951786003502875, 0.20014601215416555], index=0), Atom('Cu', [0.08453269827616121, 1.807218974303731, 1.7664733621606055], index=1), Atom('Cu', [1.9597357123378... \n",
- "11 (Atom('Cu', [10.991332780892245, 11.027168829788469, 0.17044757336885022], index=0), Atom('Cu', [10.869778028731718, 1.9053524080340238, 1.7569642307109068], index=1), Atom('Cu', [1.89027128290555... \n",
- "12 (Atom('Cu', [10.958070767872973, 0.05270018288525903, 11.015795828313184], index=0), Atom('Cu', [0.04540970504125744, 2.0780519854689388, 1.8353871076994486], index=1), Atom('Cu', [1.8285627799497... \n",
- "13 (Atom('Cu', [10.903697402270481, 10.843249424147054, 0.09574490333935876], index=0), Atom('Cu', [10.594918916668115, 1.7187193807013557, 1.957301113531466], index=1), Atom('Cu', [1.676074002401865... \n",
- "14 (Atom('Cu', [0.25841198959969514, 0.07063449316617948, 10.912283030401635], index=0), Atom('Cu', [0.19649020014428978, 1.7891621064498946, 1.803181486480956], index=1), Atom('Cu', [1.7493171890008... \n",
- "15 (Atom('Cu', [0.004994793789590358, 10.985055637636528, 10.904222762438215], index=0), Atom('Cu', [10.941729664004177, 1.8868614122932958, 1.8147512571785023], index=1), Atom('Cu', [1.8438031255437... \n",
- "16 (Atom('Cu', [0.03645923830333788, 10.856010693932728, 10.970572990196315], index=0), Atom('Cu', [10.913522535008392, 1.8161914381204696, 1.5447581077411967], index=1), Atom('Cu', [1.77583933019795... \n",
- "17 (Atom('Cu', [0.009767181825404037, 0.0071865231528328395, 11.0089023992238], index=0), Atom('Cu', [10.89374596605574, 1.9969194212313586, 1.595578837066423], index=1), Atom('Cu', [2.05442192913884... \n",
- "18 (Atom('Cu', [0.0, 1.8049999999995918, 1.804999999999592], index=0), Atom('Cu', [1.8049999999995918, 1.1052437362302365e-16, 1.804999999999592], index=1), Atom('Cu', [1.8049999999995918, 1.80499999... \n",
- "19 (Atom('Cu', [0.10390109091492504, 1.9580560553035629, 1.779188560767685], index=0), Atom('Cu', [1.3868099266682106, 10.93717630776342, 1.601790948781396], index=1), Atom('Cu', [1.7183987889409966,... \n",
- "20 (Atom('Cu', [0.2223234793002756, 1.6999920633404135, 1.7623980235259114], index=0), Atom('Cu', [2.122882660879041, 0.18624238423025943, 2.030808683988878], index=1), Atom('Cu', [1.7334689484700414... \n",
- "21 (Atom('Cu', [10.839434974930338, 1.9008464900018593, 1.6867963294684278], index=0), Atom('Cu', [1.5876598581358559, 0.15447557118789498, 1.715299994817798], index=1), Atom('Cu', [2.148606011735498... \n",
- "22 (Atom('Cu', [10.827044041159013, 1.814885520198166, 2.1241909251750846], index=0), Atom('Cu', [1.6679142700199647, 0.1106240807045643, 1.629401459425781], index=1), Atom('Cu', [1.29418686613977, 2... \n",
- "23 (Atom('Cu', [0.18579648605077317, 1.6025971043596021, 1.8725196193757527], index=0), Atom('Cu', [2.0480835165906024, 0.055238510607153124, 2.258841094259334], index=1), Atom('Cu', [1.8423446841272... \n",
- "24 (Atom('Cu', [10.902653792490328, 1.9016481867720487, 1.712386801004505], index=0), Atom('Cu', [1.6843656295425262, 0.20454959542790652, 1.4940727709027473], index=1), Atom('Cu', [1.628967729295861... \n",
- "25 (Atom('Cu', [0.2202511036922136, 1.5872348575607442, 1.7623998700282655], index=0), Atom('Cu', [1.8386689358546338, 10.988833155546024, 1.8571406735818983], index=1), Atom('Cu', [1.976390891645592... \n",
- "26 (Atom('Cu', [10.746144724861097, 1.6561216261540705, 1.6676063346982197], index=0), Atom('Cu', [1.8697696006277247, 0.29566102043388925, 1.9071090181664805], index=1), Atom('Cu', [1.82013088394316... \n",
- "27 (Atom('Cu', [0.17063059290766686, 1.8534187978406447, 1.7423748775395635], index=0), Atom('Cu', [1.8595651370150335, 10.90572322929213, 1.810598025463111], index=1), Atom('Cu', [1.934651223968069,... \n",
- "28 (Atom('Cu', [10.866365842640102, 1.8491297818376022, 2.093244182315977], index=0), Atom('Cu', [1.7536272335648937, 10.898985481755824, 1.8241252890179567], index=1), Atom('Cu', [1.994555230114336,... \n",
- "29 (Atom('Cu', [0.0, 1.4737763285740602, 9.024277317236803e-17], index=0), Atom('Cu', [2.5526554800834376, 1.4737763285740604, 2.4654784132287465e-16], index=1), Atom('Cu', [5.105310960166875, 1.4737... \n",
- "30 (Atom('Cu', [0.4126496185424086, 0.9553642998076769, 0.12158924761653131], index=0), Atom('Cu', [2.831118842513476, 1.0700158216349893, 25.916922304272767], index=1), Atom('Cu', [5.314789299948389... \n",
- "31 (Atom('Cu', [10.151562776611806, 1.6587733567107183, 26.43035938894712], index=0), Atom('Cu', [2.5212828819853748, 1.9070842851152785, 26.36203940098048], index=1), Atom('Cu', [4.715098279443367, ... \n",
- "32 (Atom('Cu', [0.19573901040697333, 1.051460534941847, 26.530022925039237], index=0), Atom('Cu', [2.995726956631421, 1.1546751485293076, 26.840478692293868], index=1), Atom('Cu', [5.923947432763936,... \n",
- "33 (Atom('Cu', [9.888379625890167, 1.4968159202602271, 26.58551059472236], index=0), Atom('Cu', [2.207544057015841, 1.290074245861123, 26.685466830386254], index=1), Atom('Cu', [4.939145903748478, 1.... \n",
- "34 (Atom('Cu', [0.3429326290376805, 1.5170285327525175, 0.14896315086845416], index=0), Atom('Cu', [3.129345868860857, 1.495102964861245, 0.01607848650845961], index=1), Atom('Cu', [5.714332108258016... \n",
- "35 (Atom('Cu', [9.95801241508363, 1.4359511579313753, 26.639342243648862], index=0), Atom('Cu', [2.2681416259098204, 1.7861270127574713, 0.0041936446239185], index=1), Atom('Cu', [5.074253116948648, ... \n",
- "36 (Atom('Cu', [10.225027496779227, 1.7848555655274232, 26.42388292769549], index=0), Atom('Cu', [2.4914869938632442, 1.861119175102061, 26.64826771541495], index=1), Atom('Cu', [4.968442272624181, 1... \n",
- "37 (Atom('Cu', [10.188116701703345, 1.3482529143763475, 26.353847647582224], index=0), Atom('Cu', [2.4559442807081986, 1.4757747059762696, 26.57379282025388], index=1), Atom('Cu', [5.077086561123585,... \n",
- "38 (Atom('Cu', [10.165259029706933, 1.7122531598094173, 26.335638965295193], index=0), Atom('Cu', [2.3513330869200098, 1.5487334168655387, 26.34258116233103], index=1), Atom('Cu', [4.95573138146394, ... \n",
- "39 (Atom('Cu', [0.36893784059756035, 1.481831927531004, 26.53504700964859], index=0), Atom('Cu', [2.6965712181950883, 1.488812429574298, 26.730123522873537], index=1), Atom('Cu', [5.668661292604278, ... \n",
- "40 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "41 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "42 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "43 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "44 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "45 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "46 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "\n",
- " energy \\\n",
- "0 -3.142019 \n",
- "1 -3.338596 \n",
- "2 -3.416929 \n",
- "3 -3.409602 \n",
- "4 -3.330215 \n",
- "5 -3.195118 \n",
- "6 -3.035358 \n",
- "7 -347.182406 \n",
- "8 -348.253665 \n",
- "9 -345.424528 \n",
- "10 -346.758349 \n",
- "11 -344.603627 \n",
- "12 -346.849801 \n",
- "13 -345.015235 \n",
- "14 -346.569097 \n",
- "15 -344.892954 \n",
- "16 -347.628843 \n",
- "17 -345.776979 \n",
- "18 -342.906251 \n",
- "19 -343.569002 \n",
- "20 -343.317892 \n",
- "21 -341.057270 \n",
- "22 -342.067006 \n",
- "23 -342.528496 \n",
- "24 -342.900954 \n",
- "25 -340.983380 \n",
- "26 -342.479965 \n",
- "27 -340.174298 \n",
- "28 -343.805462 \n",
- "29 -400.111422 \n",
- "30 -399.591699 \n",
- "31 -398.422617 \n",
- "32 -395.110340 \n",
- "33 -396.833326 \n",
- "34 -400.444368 \n",
- "35 -397.843043 \n",
- "36 -399.776798 \n",
- "37 -399.168074 \n",
- "38 -396.984690 \n",
- "39 -399.581912 \n",
- "40 -3.142019 \n",
- "41 -3.338596 \n",
- "42 -3.416929 \n",
- "43 -3.409602 \n",
- "44 -3.330215 \n",
- "45 -3.195118 \n",
- "46 -3.035358 \n",
+ " name atoms energy \\\n",
+ "0 job_a_3_4 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) -3.142019 \n",
+ "1 job_a_3_5 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) -3.338596 \n",
+ "2 job_a_3_6 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) -3.416929 \n",
+ "3 job_a_3_7 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) -3.409602 \n",
+ "4 job_a_3_8 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) -3.330215 \n",
+ "5 job_a_3_9 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) -3.195118 \n",
+ "6 job_a_4_0 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) -3.035358 \n",
"\n",
- " forces \\\n",
- "0 [[1.1869253621046177e-16, -1.7429070520896771e-16, -1.397277865277868e-16]] \n",
- "1 [[-1.92404082484227e-16, 4.231084758750405e-17, 3.6193775346684653e-17]] \n",
- "2 [[-2.9113397169423695e-17, 7.54965057835309e-17, -3.624419431643654e-17]] \n",
- "3 [[3.771496125435321e-17, 3.412312546579927e-17, -2.4310047599025677e-17]] \n",
- "4 [[-2.0545362501508919e-16, -3.5486130576273854e-17, 3.5486130576273854e-17]] \n",
- "5 [[1.6101257219667079e-16, -4.2948421129906387e-17, 4.2948421129906387e-17]] \n",
- "6 [[-5.946777565406637e-17, -1.0605082175909553e-16, -1.2946304704347008e-16]] \n",
- "7 [[-1.2656542480726799e-14, -1.46965772884755e-14, -1.61017033040167e-14], [-1.3905543383430098e-14, 4.5310977192514186e-15, 4.8333732849403796e-15], [4.9682480351975795e-15, -1.4072076837123899e-1... \n",
- "8 [[-0.21910202935187897, -0.37573419410584397, 0.43392575377979187], [0.16208168404695897, -0.00671505675904709, 1.03458554920361], [-1.2001630139266497, -0.40207322348963503, -0.45620473735655703]... \n",
- "9 [[-0.023031834879881696, 0.042841438691562095, 0.5899774836434479], [-0.5418151518759569, 0.6754733604036028, -0.5582999589284809], [-0.6011411771360858, -0.355590065329821, -0.0035901986306415582... \n",
- "10 [[-0.32237334386615796, 0.43406651671724894, -0.5886238546572939], [-0.6295919499998729, 0.07530471384300086, -0.12687342568230403], [-0.06506588733991228, 0.8782024477953109, -0.12243680387297393... \n",
- "11 [[0.45078377546738896, -0.7167806257867769, -0.320969733282763], [0.5707773027838049, -0.5494069530705199, 0.510256621543289], [-0.36439749359274193, 0.17709586752044496, 0.23127352998569298], [0.... \n",
- "12 [[-0.38409855462219894, 0.12077975249818296, 0.024465771201292483], [-0.6560764999451358, -1.05845756801878, -0.658182913095082], [-0.5103420261962529, -0.35656271154938, 0.7090309519821769], [-0.... \n",
- "13 [[0.22044015995668098, -0.2607278388818459, -0.3855032322190499], [0.7754186544660099, -0.04652340102386694, -0.26232164996063695], [0.43147792874898694, -0.9840256256911009, 0.34987911571105595],... \n",
- "14 [[-0.5020206209676749, 0.007307596544171969, 0.24586848661916993], [-1.1645196400331899, 0.1476285180684419, 0.6168850904586589], [0.7183114431792579, -0.5420093171036479, -0.06687387962480258], [... \n",
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- "16 [[0.24033044872062098, 1.5305792677179897, -0.6791236163119347], [0.05903491332573559, 0.148151392595253, 0.542468964409148], [0.07921843405670739, 0.145966157214324, 0.7342269238916159], [0.77503... \n",
- "17 [[-0.13926025211222698, -0.0920021629424733, 0.21001691285267599], [0.6767944835284789, -1.1148612203782198, 1.7165284718417098], [-0.7950973845960859, 0.2356681337270629, 0.13328556064538893], [0... \n",
- "18 [[-1.3933298959045699e-14, -0.10827842091784899, -0.108278420917849], [-0.10827842091784899, -1.3877479054990188e-14, -0.108278420917849], [-0.10827842091784899, -0.108278420917849, -1.38424758326... \n",
- "19 [[-0.45079778130218695, 0.19236747741170795, 0.23614131969524693], [1.2984317307784499, 0.04751568933291938, 0.005443425577977482], [0.48875760189522893, -0.5765163470127929, -0.5531364786691719],... \n",
- "20 [[-0.6920262907044069, 0.3986975029533409, 0.10738641941643497], [-1.2097205794553298, -2.21423543020926, -0.8363385508172672], [-0.36839592956658596, 0.35919893904268096, 0.09738195090664269], [2... \n",
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- "25 [[-0.6923371397061229, 0.9615117892959398, 0.396261839433264], [0.18083100499512797, -0.47103552335731996, 0.5072798013711759], [-0.22735248831930896, 0.37011177620569996, -1.1740345740817597], [0... \n",
- "26 [[0.46940172398488794, 0.557542533896084, 0.861383405261689], [-0.16043498489103097, -1.0997839627759998, -0.013974118313924476], [-0.13554483762266598, 0.6363641149062679, 0.153788969879775], [1.... \n",
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- "29 [[-8.770761894538741e-15, 1.15657750043852e-09, 0.109956784695642], [-1.0824674490095302e-15, 1.15657515335765e-09, 0.10995678469564], [2.4308896909297503e-15, 1.1565748914144101e-09, 0.1099567846... \n",
- "30 [[-0.570999988178131, 1.09833300989089, -0.566585257198732], [-0.0492000252908114, 0.597416506778632, 0.966635869516025], [-0.0024866801464792, -0.0674126479000064, 0.2546322145358], [0.2289085885... \n",
- "31 [[-0.555658749547497, 0.42566314828364993, 0.466167261187443], [-1.39449792316952, -0.5017985009363372, 0.4456531170887249], [1.25410857633035, -0.33111996310077596, -0.24658722383829995], [0.3911... \n",
- "32 [[0.550884754551829, 0.33865983742272204, 0.12042096866985205], [0.189727589275692, -0.524367356684999, -0.17886090450862402], [-1.6758650723099, -0.31030261483377614, 0.6946509585213989], [0.4453... \n",
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- "36 [[0.552749731038993, -0.46087407707755496, 0.177277816519943], [0.340079509783844, 0.23574679643440802, 0.26784307872713603], [0.388298578408562, 0.331969932822417, 0.34399524632239104], [-0.59826... \n",
- "37 [[0.570532228156976, 0.17193739345012202, 0.796311675035985], [0.67297011060888, -0.81773224395498, 0.207420342553107], [0.391385096561351, -0.862073619446019, 0.597813478897486], [-0.270452726779... \n",
- "38 [[-1.11093024791963, -0.6986058376048401, 0.44674121624886787], [0.0416051560041239, -0.0424634970331458, -0.0874515615419252], [-0.135122617155289, -0.778975629913174, -1.04402814519501], [0.8442... \n",
- "39 [[-0.645092069902244, 0.04920122139904896, 0.21081031345366397], [1.14893686034324, -0.6114894792387109, -0.40665475671972495], [-0.287092609914331, 0.292443458702414, -0.801010316546939], [-0.184... \n",
- "40 [[1.1869253621046177e-16, -1.7429070520896771e-16, -1.397277865277868e-16]] \n",
- "41 [[-1.92404082484227e-16, 4.231084758750405e-17, 3.6193775346684653e-17]] \n",
- "42 [[-2.9113397169423695e-17, 7.54965057835309e-17, -3.624419431643654e-17]] \n",
- "43 [[3.771496125435321e-17, 3.412312546579927e-17, -2.4310047599025677e-17]] \n",
- "44 [[-2.0545362501508919e-16, -3.5486130576273854e-17, 3.5486130576273854e-17]] \n",
- "45 [[1.6101257219667079e-16, -4.2948421129906387e-17, 4.2948421129906387e-17]] \n",
- "46 [[-5.946777565406637e-17, -1.0605082175909553e-16, -1.2946304704347008e-16]] \n",
+ " forces \\\n",
+ "0 [[1.1869253621046177e-16, -1.7429070520896771e-16, -1.397277865277868e-16]] \n",
+ "1 [[-1.92404082484227e-16, 4.231084758750405e-17, 3.6193775346684653e-17]] \n",
+ "2 [[-2.9113397169423695e-17, 7.54965057835309e-17, -3.624419431643654e-17]] \n",
+ "3 [[3.771496125435321e-17, 3.412312546579927e-17, -2.4310047599025677e-17]] \n",
+ "4 [[-2.0545362501508919e-16, -3.5486130576273854e-17, 3.5486130576273854e-17]] \n",
+ "5 [[1.6101257219667079e-16, -4.2948421129906387e-17, 4.2948421129906387e-17]] \n",
+ "6 [[-5.946777565406637e-17, -1.0605082175909553e-16, -1.2946304704347008e-16]] \n",
"\n",
- " number_of_atoms \n",
- "0 1.0 \n",
- "1 1.0 \n",
- "2 1.0 \n",
- "3 1.0 \n",
- "4 1.0 \n",
- "5 1.0 \n",
- "6 1.0 \n",
- "7 108.0 \n",
- "8 108.0 \n",
- "9 108.0 \n",
- "10 108.0 \n",
- "11 108.0 \n",
- "12 108.0 \n",
- "13 108.0 \n",
- "14 108.0 \n",
- "15 108.0 \n",
- "16 108.0 \n",
- "17 108.0 \n",
- "18 107.0 \n",
- "19 107.0 \n",
- "20 107.0 \n",
- "21 107.0 \n",
- "22 107.0 \n",
- "23 107.0 \n",
- "24 107.0 \n",
- "25 107.0 \n",
- "26 107.0 \n",
- "27 107.0 \n",
- "28 107.0 \n",
- "29 128.0 \n",
- "30 128.0 \n",
- "31 128.0 \n",
- "32 128.0 \n",
- "33 128.0 \n",
- "34 128.0 \n",
- "35 128.0 \n",
- "36 128.0 \n",
- "37 128.0 \n",
- "38 128.0 \n",
- "39 128.0 \n",
- "40 1.0 \n",
- "41 1.0 \n",
- "42 1.0 \n",
- "43 1.0 \n",
- "44 1.0 \n",
- "45 1.0 \n",
- "46 1.0 "
+ " number_of_atoms \n",
+ "0 1.0 \n",
+ "1 1.0 \n",
+ "2 1.0 \n",
+ "3 1.0 \n",
+ "4 1.0 \n",
+ "5 1.0 \n",
+ "6 1.0 "
]
},
- "execution_count": 18,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -806,7 +268,7 @@
},
{
"cell_type": "markdown",
- "id": "imperial-belarus",
+ "id": "signed-instrument",
"metadata": {},
"source": [
"## **Add structures from the MD**\n",
@@ -816,8 +278,8 @@
},
{
"cell_type": "code",
- "execution_count": 20,
- "id": "healthy-structure",
+ "execution_count": 8,
+ "id": "removed-consent",
"metadata": {},
"outputs": [],
"source": [
@@ -827,8 +289,8 @@
},
{
"cell_type": "code",
- "execution_count": 21,
- "id": "outside-bhutan",
+ "execution_count": 9,
+ "id": "fewer-average",
"metadata": {},
"outputs": [],
"source": [
@@ -843,7 +305,7 @@
},
{
"cell_type": "markdown",
- "id": "minus-blink",
+ "id": "gentle-manufacturer",
"metadata": {},
"source": [
"## **Add some defect structures (vacancies, surfaces, etc)**\n",
@@ -853,15 +315,15 @@
},
{
"cell_type": "code",
- "execution_count": 22,
- "id": "loaded-sheriff",
+ "execution_count": 10,
+ "id": "leading-julian",
"metadata": {},
"outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "2021-03-08 11:55:40,043 - pyiron_log - WARNING - The job lammps_job_vac is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
+ "The job lammps_job_vac was saved and received the ID: 52\n"
]
}
],
@@ -877,15 +339,15 @@
},
{
"cell_type": "code",
- "execution_count": 28,
- "id": "billion-shade",
+ "execution_count": 11,
+ "id": "adjacent-panama",
"metadata": {},
"outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "2021-03-08 11:56:36,874 - pyiron_log - WARNING - The job lammps_job_surf is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
+ "The job lammps_job_surf was saved and received the ID: 53\n"
]
}
],
@@ -900,17 +362,17 @@
},
{
"cell_type": "code",
- "execution_count": 29,
- "id": "constitutional-throw",
+ "execution_count": 12,
+ "id": "encouraging-credit",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'groups': ['tc'], 'nodes': ['lammps_job_vac', 'lammps_job_surf', 'dataset_example']}"
+ "{'groups': [], 'nodes': ['lammps_job_vac', 'lammps_job_surf']}"
]
},
- "execution_count": 29,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -921,7 +383,7 @@
},
{
"cell_type": "markdown",
- "id": "alpine-cooking",
+ "id": "humanitarian-effect",
"metadata": {},
"source": [
"We now add these structures to the dataset"
@@ -929,8 +391,8 @@
},
{
"cell_type": "code",
- "execution_count": 33,
- "id": "italian-cleanup",
+ "execution_count": 13,
+ "id": "floating-utility",
"metadata": {},
"outputs": [],
"source": [
@@ -945,15 +407,26 @@
},
{
"cell_type": "code",
- "execution_count": 34,
- "id": "exotic-asset",
+ "execution_count": 14,
+ "id": "excited-digest",
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job dataset_example was saved and received the ID: 54\n"
+ ]
+ },
{
"name": "stderr",
"output_type": "stream",
"text": [
- "2021-03-08 11:59:00,874 - pyiron_log - WARNING - The job dataset_example is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
+ "/opt/conda/lib/python3.8/site-packages/pandas/core/generic.py:2606: PerformanceWarning: \n",
+ "your performance may suffer as PyTables will pickle object types that it cannot\n",
+ "map directly to c-types [inferred_type->mixed,key->block1_values] [items->Index(['name', 'atoms', 'forces'], dtype='object')]\n",
+ "\n",
+ " pytables.to_hdf(\n"
]
}
],
@@ -964,8 +437,8 @@
},
{
"cell_type": "code",
- "execution_count": 32,
- "id": "british-kelly",
+ "execution_count": 15,
+ "id": "western-frank",
"metadata": {},
"outputs": [
{
@@ -1009,17 +482,17 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>4332</td>\n",
+ " <td>52</td>\n",
" <td>finished</td>\n",
" <td>Cu107</td>\n",
" <td>lammps_job_vac</td>\n",
" <td>/lammps_job_vac</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/creating_datasets/</td>\n",
- " <td>2021-03-04 15:54:10.622737</td>\n",
- " <td>2021-03-04 15:54:15.859551</td>\n",
- " <td>5.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/creating_datasets/</td>\n",
+ " <td>2021-03-09 09:00:43.012322</td>\n",
+ " <td>2021-03-09 09:00:47.308799</td>\n",
+ " <td>4.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1027,17 +500,17 @@
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>4333</td>\n",
+ " <td>53</td>\n",
" <td>finished</td>\n",
" <td>Cu128</td>\n",
" <td>lammps_job_surf</td>\n",
" <td>/lammps_job_surf</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/creating_datasets/</td>\n",
- " <td>2021-03-04 15:54:16.720902</td>\n",
- " <td>2021-03-04 15:54:21.579389</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/creating_datasets/</td>\n",
+ " <td>2021-03-09 09:00:48.219183</td>\n",
+ " <td>2021-03-09 09:00:52.902683</td>\n",
" <td>4.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1045,17 +518,17 @@
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>4334</td>\n",
+ " <td>54</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>dataset_example</td>\n",
" <td>/dataset_example</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/day_1/creating_datasets/</td>\n",
- " <td>2021-03-04 15:54:23.919785</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/creating_datasets/</td>\n",
+ " <td>2021-03-09 09:01:01.281092</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>TrainingContainer</td>\n",
" <td>0.4</td>\n",
" <td>None</td>\n",
@@ -1066,28 +539,28 @@
"</div>"
],
"text/plain": [
- " id status chemicalformula job subjob \\\n",
- "0 4332 finished Cu107 lammps_job_vac /lammps_job_vac \n",
- "1 4333 finished Cu128 lammps_job_surf /lammps_job_surf \n",
- "2 4334 finished None dataset_example /dataset_example \n",
+ " id status chemicalformula job subjob \\\n",
+ "0 52 finished Cu107 lammps_job_vac /lammps_job_vac \n",
+ "1 53 finished Cu128 lammps_job_surf /lammps_job_surf \n",
+ "2 54 finished None dataset_example /dataset_example \n",
"\n",
- " projectpath project \\\n",
- "0 /home/surendralal/ notebooks/pyiron_potentialfit/day_1/creating_datasets/ \n",
- "1 /home/surendralal/ notebooks/pyiron_potentialfit/day_1/creating_datasets/ \n",
- "2 /home/surendralal/ notebooks/pyiron_potentialfit/day_1/creating_datasets/ \n",
+ " projectpath project timestart \\\n",
+ "0 /home/pyiron/ day_1/creating_datasets/ 2021-03-09 09:00:43.012322 \n",
+ "1 /home/pyiron/ day_1/creating_datasets/ 2021-03-09 09:00:48.219183 \n",
+ "2 /home/pyiron/ day_1/creating_datasets/ 2021-03-09 09:01:01.281092 \n",
"\n",
- " timestart timestop totalcputime \\\n",
- "0 2021-03-04 15:54:10.622737 2021-03-04 15:54:15.859551 5.0 \n",
- "1 2021-03-04 15:54:16.720902 2021-03-04 15:54:21.579389 4.0 \n",
- "2 2021-03-04 15:54:23.919785 NaT NaN \n",
+ " timestop totalcputime computer \\\n",
+ "0 2021-03-09 09:00:47.308799 4.0 pyiron@jupyter-janssen#1 \n",
+ "1 2021-03-09 09:00:52.902683 4.0 pyiron@jupyter-janssen#1 \n",
+ "2 NaT NaN pyiron@jupyter-janssen#1 \n",
"\n",
- " computer hamilton hamversion parentid masterid \n",
- "0 pyiron@cmdell17#1 Lammps 0.1 None None \n",
- "1 pyiron@cmdell17#1 Lammps 0.1 None None \n",
- "2 pyiron@cmdell17#1 TrainingContainer 0.4 None None "
+ " hamilton hamversion parentid masterid \n",
+ "0 Lammps 0.1 None None \n",
+ "1 Lammps 0.1 None None \n",
+ "2 TrainingContainer 0.4 None None "
]
},
- "execution_count": 32,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -1098,7 +571,7 @@
},
{
"cell_type": "markdown",
- "id": "ideal-volunteer",
+ "id": "valuable-procedure",
"metadata": {},
"source": [
"## **Reloading the dataset**\n",
@@ -1108,8 +581,8 @@
},
{
"cell_type": "code",
- "execution_count": 35,
- "id": "numeric-museum",
+ "execution_count": 16,
+ "id": "exempt-sydney",
"metadata": {},
"outputs": [
{
@@ -1143,7 +616,7 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>None</td>\n",
+ " <td>job_a_3_4</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.142019</td>\n",
" <td>[[1.1869253621046177e-16, -1.7429070520896771e-16, -1.397277865277868e-16]]</td>\n",
@@ -1151,7 +624,7 @@
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>None</td>\n",
+ " <td>job_a_3_5</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.338596</td>\n",
" <td>[[-1.92404082484227e-16, 4.231084758750405e-17, 3.6193775346684653e-17]]</td>\n",
@@ -1159,7 +632,7 @@
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>None</td>\n",
+ " <td>job_a_3_6</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.416929</td>\n",
" <td>[[-2.9113397169423695e-17, 7.54965057835309e-17, -3.624419431643654e-17]]</td>\n",
@@ -1167,7 +640,7 @@
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>None</td>\n",
+ " <td>job_a_3_7</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.409602</td>\n",
" <td>[[3.771496125435321e-17, 3.412312546579927e-17, -2.4310047599025677e-17]]</td>\n",
@@ -1175,7 +648,7 @@
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>None</td>\n",
+ " <td>job_a_3_8</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.330215</td>\n",
" <td>[[-2.0545362501508919e-16, -3.5486130576273854e-17, 3.5486130576273854e-17]]</td>\n",
@@ -1183,7 +656,7 @@
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
- " <td>None</td>\n",
+ " <td>job_a_3_9</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.195118</td>\n",
" <td>[[1.6101257219667079e-16, -4.2948421129906387e-17, 4.2948421129906387e-17]]</td>\n",
@@ -1191,7 +664,7 @@
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
- " <td>None</td>\n",
+ " <td>job_a_4_0</td>\n",
" <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0))</td>\n",
" <td>-3.035358</td>\n",
" <td>[[-5.946777565406637e-17, -1.0605082175909553e-16, -1.2946304704347008e-16]]</td>\n",
@@ -1199,266 +672,266 @@
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0), Atom('Cu', [0.0, 1.8049999999995918, 1.804999999999592], index=1), Atom('Cu', [1.8049999999995918, 1.1052437362302365e-16, 1.804999999999592], index=2), Atom...</td>\n",
- " <td>-347.182406</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>(Atom('Cu', [0.0, 0.0, 0.0], index=0), Atom('Cu', [0.0, 1.804999999999592, 1.804999999999592], index=1), Atom('Cu', [1.804999999999592, 1.1052437362302367e-16, 1.804999999999592], index=2), Atom('...</td>\n",
+ " <td>-369.311743</td>\n",
" <td>[[-1.2656542480726799e-14, -1.46965772884755e-14, -1.61017033040167e-14], [-1.3905543383430098e-14, 4.5310977192514186e-15, 4.8333732849403796e-15], [4.9682480351975795e-15, -1.4072076837123899e-1...</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.140426153531212, 11.00934611760493, 10.968207696001379], index=0), Atom('Cu', [10.983357200359302, 1.779939365335074, 1.7146804782560905], index=1), Atom('Cu', [2.1228644677344763, ...</td>\n",
- " <td>-348.253665</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>(Atom('Cu', [0.140426153531212, 11.00934611760493, 10.96820769600138], index=0), Atom('Cu', [10.983357200359302, 1.779939365335074, 1.7146804782560903], index=1), Atom('Cu', [2.1228644677344763, 0...</td>\n",
+ " <td>-360.190839</td>\n",
" <td>[[-0.21910202935187897, -0.37573419410584397, 0.43392575377979187], [0.16208168404695897, -0.00671505675904709, 1.03458554920361], [-1.2001630139266497, -0.40207322348963503, -0.45620473735655703]...</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.1407579358923329, 11.020287239626356, 10.855878337455094], index=0), Atom('Cu', [0.29542104007972325, 1.6514729828183248, 1.7760939949715002], index=1), Atom('Cu', [1.99578258187561...</td>\n",
- " <td>-345.424528</td>\n",
- " <td>[[-0.023031834879881696, 0.042841438691562095, 0.5899774836434479], [-0.5418151518759569, 0.6754733604036028, -0.5582999589284809], [-0.6011411771360858, -0.355590065329821, -0.0035901986306415582...</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>(Atom('Cu', [0.1407579358923329, 11.020287239626356, 10.855878337455094], index=0), Atom('Cu', [0.29542104007972325, 1.6514729828183248, 1.7760939949715], index=1), Atom('Cu', [1.9957825818756145,...</td>\n",
+ " <td>-356.403521</td>\n",
+ " <td>[[-0.023031834879864897, 0.04284143869144259, 0.5899774836434099], [-0.5418151518758109, 0.6754733604037649, -0.5582999589285099], [-0.6011411771363389, -0.355590065330048, -0.003590198630652358],...</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.04675277832683521, 10.951786003502875, 0.20014601215416555], index=0), Atom('Cu', [0.08453269827616121, 1.807218974303731, 1.7664733621606055], index=1), Atom('Cu', [1.9597357123378...</td>\n",
- " <td>-346.758349</td>\n",
- " <td>[[-0.32237334386615796, 0.43406651671724894, -0.5886238546572939], [-0.6295919499998729, 0.07530471384300086, -0.12687342568230403], [-0.06506588733991228, 0.8782024477953109, -0.12243680387297393...</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>(Atom('Cu', [0.046752778326835207, 10.951786003502875, 0.20014601215416555], index=0), Atom('Cu', [0.08453269827616121, 1.8072189743037306, 1.7664733621606052], index=1), Atom('Cu', [1.95973571233...</td>\n",
+ " <td>-358.245754</td>\n",
+ " <td>[[-0.32237334386617594, 0.43406651671772695, -0.5886238546573999], [-0.6295919499999019, 0.07530471384292876, -0.12687342568255203], [-0.06506588733974998, 0.8782024477947719, -0.12243680387296693...</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.991332780892245, 11.027168829788469, 0.17044757336885022], index=0), Atom('Cu', [10.869778028731718, 1.9053524080340238, 1.7569642307109068], index=1), Atom('Cu', [1.89027128290555...</td>\n",
- " <td>-344.603627</td>\n",
- " <td>[[0.45078377546738896, -0.7167806257867769, -0.320969733282763], [0.5707773027838049, -0.5494069530705199, 0.510256621543289], [-0.36439749359274193, 0.17709586752044496, 0.23127352998569298], [0....</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>(Atom('Cu', [10.99133278089223, 11.027168829788456, 0.17044757336885], index=0), Atom('Cu', [10.869778028731703, 1.9053524080340212, 1.7569642307109046], index=1), Atom('Cu', [1.8902712829055568, ...</td>\n",
+ " <td>-356.564325</td>\n",
+ " <td>[[0.45078377546780296, -0.7167806257868728, -0.320969733281809], [0.5707773027839779, -0.5494069530720159, 0.510256621543522], [-0.36439749359319995, 0.17709586752113193, 0.23127352998529296], [0....</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.958070767872973, 0.05270018288525903, 11.015795828313184], index=0), Atom('Cu', [0.04540970504125744, 2.0780519854689388, 1.8353871076994486], index=1), Atom('Cu', [1.8285627799497...</td>\n",
- " <td>-346.849801</td>\n",
- " <td>[[-0.38409855462219894, 0.12077975249818296, 0.024465771201292483], [-0.6560764999451358, -1.05845756801878, -0.658182913095082], [-0.5103420261962529, -0.35656271154938, 0.7090309519821769], [-0....</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>(Atom('Cu', [10.958070767873476, 0.05270018288526145, 11.015795828313692], index=0), Atom('Cu', [0.045409705041259525, 2.078051985469034, 1.8353871076995325], index=1), Atom('Cu', [1.8285627799498...</td>\n",
+ " <td>-357.011799</td>\n",
+ " <td>[[-0.38409855462373593, 0.12077975249587695, 0.02446577119927368], [-0.6560764999470009, -1.05845756801682, -0.658182913092867], [-0.5103420261931418, -0.356562711546522, 0.7090309519858399], [-0....</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.903697402270481, 10.843249424147054, 0.09574490333935876], index=0), Atom('Cu', [10.594918916668115, 1.7187193807013557, 1.957301113531466], index=1), Atom('Cu', [1.676074002401865...</td>\n",
- " <td>-345.015235</td>\n",
- " <td>[[0.22044015995668098, -0.2607278388818459, -0.3855032322190499], [0.7754186544660099, -0.04652340102386694, -0.26232164996063695], [0.43147792874898694, -0.9840256256911009, 0.34987911571105595],...</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>(Atom('Cu', [10.903697402270652, 10.843249424146602, 0.09574490333936027], index=0), Atom('Cu', [10.59491891666766, 1.7187193807026324, 1.9573011135302476], index=1), Atom('Cu', [1.676074002401891...</td>\n",
+ " <td>-357.856759</td>\n",
+ " <td>[[0.22044015996419397, -0.26072783887691897, -0.38550323222201893], [0.7754186544628359, -0.04652340102588245, -0.2623216499598349], [0.4314779287479719, -0.9840256256937749, 0.34987911571166197],...</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.25841198959969514, 0.07063449316617948, 10.912283030401635], index=0), Atom('Cu', [0.19649020014428978, 1.7891621064498946, 1.803181486480956], index=1), Atom('Cu', [1.7493171890008...</td>\n",
- " <td>-346.569097</td>\n",
- " <td>[[-0.5020206209676749, 0.007307596544171969, 0.24586848661916993], [-1.1645196400331899, 0.1476285180684419, 0.6168850904586589], [0.7183114431792579, -0.5420093171036479, -0.06687387962480258], [...</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>(Atom('Cu', [0.258411989603464, 0.0706344931649224, 10.912283030397811], index=0), Atom('Cu', [0.1964902001455455, 1.7891621064461172, 1.8031814864809468], index=1), Atom('Cu', [1.7493171890021084...</td>\n",
+ " <td>-358.316140</td>\n",
+ " <td>[[-0.5020206209738959, 0.007307596546447969, 0.24586848662953095], [-1.1645196400318298, 0.14762851807991492, 0.6168850904682569], [0.7183114431738189, -0.5420093170980669, -0.06687387962120929], ...</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.004994793789590358, 10.985055637636528, 10.904222762438215], index=0), Atom('Cu', [10.941729664004177, 1.8868614122932958, 1.8147512571785023], index=1), Atom('Cu', [1.8438031255437...</td>\n",
- " <td>-344.892954</td>\n",
- " <td>[[0.04806202889719739, 0.48969724353819394, 1.29371057615331], [0.5538169933185199, -0.7855310261714289, -0.033081946412792815], [0.34832740609982993, 0.9937361742308158, 0.30650548838004904], [-0...</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>(Atom('Cu', [0.004994793782076363, 10.985055637641707, 10.904222762435255], index=0), Atom('Cu', [10.9417296640056, 1.886861412294462, 1.8147512571859394], index=1), Atom('Cu', [1.8438031255436442...</td>\n",
+ " <td>-356.847816</td>\n",
+ " <td>[[0.04806202890911529, 0.4896972435411299, 1.29371057618316], [0.5538169933312109, -0.7855310261686719, -0.033081946439222513], [0.34832740608971496, 0.9937361742507679, 0.30650548838579206], [-0....</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.03645923830333788, 10.856010693932728, 10.970572990196315], index=0), Atom('Cu', [10.913522535008392, 1.8161914381204696, 1.5447581077411967], index=1), Atom('Cu', [1.77583933019795...</td>\n",
- " <td>-347.628843</td>\n",
- " <td>[[0.24033044872062098, 1.5305792677179897, -0.6791236163119347], [0.05903491332573559, 0.148151392595253, 0.542468964409148], [0.07921843405670739, 0.145966157214324, 0.7342269238916159], [0.77503...</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>(Atom('Cu', [0.03645923831331586, 10.8560106939291, 10.97057299019823], index=0), Atom('Cu', [10.913522535013476, 1.816191438106623, 1.5447581077438086], index=1), Atom('Cu', [1.7758393302091413, ...</td>\n",
+ " <td>-358.626624</td>\n",
+ " <td>[[0.24033044872932896, 1.5305792676779497, -0.6791236163998687], [0.05903491330127059, 0.148151392634253, 0.542468964385662], [0.07921843405774338, 0.145966157230826, 0.7342269239659519], [0.77503...</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.009767181825404037, 0.0071865231528328395, 11.0089023992238], index=0), Atom('Cu', [10.89374596605574, 1.9969194212313586, 1.595578837066423], index=1), Atom('Cu', [2.05442192913884...</td>\n",
- " <td>-345.776979</td>\n",
- " <td>[[-0.13926025211222698, -0.0920021629424733, 0.21001691285267599], [0.6767944835284789, -1.1148612203782198, 1.7165284718417098], [-0.7950973845960859, 0.2356681337270629, 0.13328556064538893], [0...</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>(Atom('Cu', [0.00976718182041884, 0.007186523165377699, 11.008902399259645], index=0), Atom('Cu', [10.89374596607187, 1.9969194212527828, 1.5955788370755422], index=1), Atom('Cu', [2.0544219291027...</td>\n",
+ " <td>-356.796173</td>\n",
+ " <td>[[-0.13926025200196399, -0.0920021630565197, 0.21001691279918497], [0.6767944835632749, -1.1148612202170798, 1.7165284720013199], [-0.7950973844667609, 0.2356681335380539, 0.13328556065954994], [0...</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 1.8049999999995918, 1.804999999999592], index=0), Atom('Cu', [1.8049999999995918, 1.1052437362302365e-16, 1.804999999999592], index=1), Atom('Cu', [1.8049999999995918, 1.80499999...</td>\n",
- " <td>-342.906251</td>\n",
+ " <td>lammps_job_vac</td>\n",
+ " <td>(Atom('Cu', [0.0, 1.804999999999592, 1.804999999999592], index=0), Atom('Cu', [1.804999999999592, 1.1052437362302367e-16, 1.804999999999592], index=1), Atom('Cu', [1.804999999999592, 1.80499999999...</td>\n",
+ " <td>-364.828772</td>\n",
" <td>[[-1.3933298959045699e-14, -0.10827842091784899, -0.108278420917849], [-0.10827842091784899, -1.3877479054990188e-14, -0.108278420917849], [-0.10827842091784899, -0.108278420917849, -1.38424758326...</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.10390109091492504, 1.9580560553035629, 1.779188560767685], index=0), Atom('Cu', [1.3868099266682106, 10.93717630776342, 1.601790948781396], index=1), Atom('Cu', [1.7183987889409966,...</td>\n",
- " <td>-343.569002</td>\n",
- " <td>[[-0.45079778130218695, 0.19236747741170795, 0.23614131969524693], [1.2984317307784499, 0.04751568933291938, 0.005443425577977482], [0.48875760189522893, -0.5765163470127929, -0.5531364786691719],...</td>\n",
+ " <td>lammps_job_vac</td>\n",
+ " <td>(Atom('Cu', [0.10390109091492468, 1.9580560553035562, 1.7791885607676787], index=0), Atom('Cu', [1.3868099266682057, 10.93717630776338, 1.6017909487813904], index=1), Atom('Cu', [1.718398788940990...</td>\n",
+ " <td>-353.817356</td>\n",
+ " <td>[[-0.45079778130212095, 0.19236747741186794, 0.23614131969518395], [1.2984317307785798, 0.047515689332818876, 0.0054434255778954815], [0.48875760189529993, -0.5765163470127389, -0.5531364786691438...</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.2223234793002756, 1.6999920633404135, 1.7623980235259114], index=0), Atom('Cu', [2.122882660879041, 0.18624238423025943, 2.030808683988878], index=1), Atom('Cu', [1.7334689484700414...</td>\n",
- " <td>-343.317892</td>\n",
- " <td>[[-0.6920262907044069, 0.3986975029533409, 0.10738641941643497], [-1.2097205794553298, -2.21423543020926, -0.8363385508172672], [-0.36839592956658596, 0.35919893904268096, 0.09738195090664269], [2...</td>\n",
+ " <td>lammps_job_vac</td>\n",
+ " <td>(Atom('Cu', [0.222323479300276, 1.6999920633404162, 1.7623980235259142], index=0), Atom('Cu', [2.1228826608790445, 0.1862423842302597, 2.0308086839888815], index=1), Atom('Cu', [1.733468948470044,...</td>\n",
+ " <td>-353.115830</td>\n",
+ " <td>[[-0.6920262907037699, 0.3986975029534239, 0.10738641941633598], [-1.2097205794551897, -2.2142354302088, -0.8363385508175151], [-0.36839592956672496, 0.3591989390425319, 0.09738195090699889], [2.1...</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.839434974930338, 1.9008464900018593, 1.6867963294684278], index=0), Atom('Cu', [1.5876598581358559, 0.15447557118789498, 1.715299994817798], index=1), Atom('Cu', [2.148606011735498...</td>\n",
- " <td>-341.057270</td>\n",
- " <td>[[0.5607305368018309, 0.07454461225822963, 0.17092757180402804], [0.7460358156721709, -0.9693709228665989, -0.357775642488428], [-1.4982109077380097, 1.0695224870660298, 0.17016025464137993], [1.1...</td>\n",
+ " <td>lammps_job_vac</td>\n",
+ " <td>(Atom('Cu', [10.839434974930233, 1.9008464900018414, 1.6867963294684114], index=0), Atom('Cu', [1.587659858135841, 0.1544755711878935, 1.7152999948177818], index=1), Atom('Cu', [2.148606011735478,...</td>\n",
+ " <td>-352.375089</td>\n",
+ " <td>[[0.5607305368014349, 0.07454461225798582, 0.17092757180355503], [0.7460358156722969, -0.9693709228667519, -0.357775642489466], [-1.4982109077379697, 1.0695224870646098, 0.17016025464195195], [1.1...</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.827044041159013, 1.814885520198166, 2.1241909251750846], index=0), Atom('Cu', [1.6679142700199647, 0.1106240807045643, 1.629401459425781], index=1), Atom('Cu', [1.29418686613977, 2...</td>\n",
- " <td>-342.067006</td>\n",
- " <td>[[0.7768634672312369, 0.615614277472231, -0.6520778674544528], [0.5196497874591819, -0.7614150189249069, 0.33452284258026094], [1.0185593798470798, 0.11349094338622906, -1.1138567887728799], [0.39...</td>\n",
+ " <td>lammps_job_vac</td>\n",
+ " <td>(Atom('Cu', [10.827044041158409, 1.8148855201981695, 2.124190925175089], index=0), Atom('Cu', [1.6679142700199683, 0.11062408070456453, 1.6294014594257846], index=1), Atom('Cu', [1.294186866136013...</td>\n",
+ " <td>-352.774500</td>\n",
+ " <td>[[0.7768634672242999, 0.615614277463985, -0.6520778674493769], [0.5196497874591279, -0.7614150189291738, 0.33452284258293197], [1.0185593798557397, 0.11349094339204105, -1.1138567887771997], [0.39...</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.18579648605077317, 1.6025971043596021, 1.8725196193757527], index=0), Atom('Cu', [2.0480835165906024, 0.055238510607153124, 2.258841094259334], index=1), Atom('Cu', [1.8423446841272...</td>\n",
- " <td>-342.528496</td>\n",
- " <td>[[-1.2511641505681497, 0.045660252990561916, 0.39426999337691987], [-0.4923847861277789, -0.676187288680257, -1.3802167704923], [-0.12072100498159098, 0.5666924914439229, -0.8825937576849239], [0....</td>\n",
+ " <td>lammps_job_vac</td>\n",
+ " <td>(Atom('Cu', [0.18579648603934987, 1.6025971043768155, 1.8725196193702205], index=0), Atom('Cu', [2.048083516582408, 0.05523851059960332, 2.2588410942434316], index=1), Atom('Cu', [1.84234468412923...</td>\n",
+ " <td>-352.466296</td>\n",
+ " <td>[[-1.2511641504900297, 0.045660252979352216, 0.3942699933548249], [-0.49238478619866294, -0.676187288648693, -1.38021677052805], [-0.12072100498243699, 0.5666924914313989, -0.8825937576412469], [0...</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.902653792490328, 1.9016481867720487, 1.712386801004505], index=0), Atom('Cu', [1.6843656295425262, 0.20454959542790652, 1.4940727709027473], index=1), Atom('Cu', [1.628967729295861...</td>\n",
- " <td>-342.900954</td>\n",
- " <td>[[0.5080309295986539, -0.4875222169174689, 0.46898176405272696], [0.11031309252296198, -0.7243436014688799, 1.2808414836059598], [0.6855285827079979, 0.576817447055947, 0.31599931473888204], [0.50...</td>\n",
+ " <td>lammps_job_vac</td>\n",
+ " <td>(Atom('Cu', [10.902653792488254, 1.9016481867624628, 1.7123868009764087], index=0), Atom('Cu', [1.684365629566867, 0.20454959543267073, 1.4940727709048371], index=1), Atom('Cu', [1.628967729292809...</td>\n",
+ " <td>-353.659518</td>\n",
+ " <td>[[0.5080309295530009, -0.4875222168854389, 0.46898176419400794], [0.11031309245877298, -0.7243436015235899, 1.2808414835860897], [0.6855285827566528, 0.576817447011234, 0.31599931467165404], [0.50...</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.2202511036922136, 1.5872348575607442, 1.7623998700282655], index=0), Atom('Cu', [1.8386689358546338, 10.988833155546024, 1.8571406735818983], index=1), Atom('Cu', [1.976390891645592...</td>\n",
- " <td>-340.983380</td>\n",
- " <td>[[-0.6923371397061229, 0.9615117892959398, 0.396261839433264], [0.18083100499512797, -0.47103552335731996, 0.5072798013711759], [-0.22735248831930896, 0.37011177620569996, -1.1740345740817597], [0...</td>\n",
+ " <td>lammps_job_vac</td>\n",
+ " <td>(Atom('Cu', [0.22025110375749696, 1.5872348575731654, 1.7623998700921586], index=0), Atom('Cu', [1.8386689358494586, 10.988833155464064, 1.8571406735076377], index=1), Atom('Cu', [1.97639089167179...</td>\n",
+ " <td>-352.032412</td>\n",
+ " <td>[[-0.6923371397932379, 0.9615117891395759, 0.396261839405581], [0.18083100479582398, -0.47103552318232994, 0.5072798015385259], [-0.22735248852514198, 0.3701117760834369, -1.1740345739673297], [0....</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.746144724861097, 1.6561216261540705, 1.6676063346982197], index=0), Atom('Cu', [1.8697696006277247, 0.29566102043388925, 1.9071090181664805], index=1), Atom('Cu', [1.82013088394316...</td>\n",
- " <td>-342.479965</td>\n",
- " <td>[[0.46940172398488794, 0.557542533896084, 0.861383405261689], [-0.16043498489103097, -1.0997839627759998, -0.013974118313924476], [-0.13554483762266598, 0.6363641149062679, 0.153788969879775], [1....</td>\n",
+ " <td>lammps_job_vac</td>\n",
+ " <td>(Atom('Cu', [10.746144724792629, 1.6561216261325158, 1.6676063346541934], index=0), Atom('Cu', [1.8697696007010116, 0.2956610204486699, 1.9071090181785917], index=1), Atom('Cu', [1.820130883905895...</td>\n",
+ " <td>-352.885609</td>\n",
+ " <td>[[0.4694017239681809, 0.557542533620799, 0.861383405389437], [-0.16043498520491098, -1.0997839630796997, -0.013974118048379877], [-0.13554483753545396, 0.6363641150344139, 0.153788969504449], [1.0...</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.17063059290766686, 1.8534187978406447, 1.7423748775395635], index=0), Atom('Cu', [1.8595651370150335, 10.90572322929213, 1.810598025463111], index=1), Atom('Cu', [1.934651223968069,...</td>\n",
- " <td>-340.174298</td>\n",
- " <td>[[-1.1653987834207897, -0.07515193371140247, -0.13414295102360305], [0.17707911646472896, 0.37588362216028093, 0.10312993516925903], [0.6003632691780849, 0.3869695806628, 0.03431754153949555], [-0...</td>\n",
+ " <td>lammps_job_vac</td>\n",
+ " <td>(Atom('Cu', [0.17063059334564, 1.853418797177199, 1.7423748774412284], index=0), Atom('Cu', [1.8595651381460672, 10.905723229638589, 1.810598025743776], index=1), Atom('Cu', [1.9346512242265819, 1...</td>\n",
+ " <td>-352.295316</td>\n",
+ " <td>[[-1.1653987878668197, -0.07515193358861387, -0.13414295175130306], [0.17707911415253696, 0.37588361844850293, 0.10312993216846503], [0.6003632668071709, 0.386969577632806, 0.03431754138154845], [...</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.866365842640102, 1.8491297818376022, 2.093244182315977], index=0), Atom('Cu', [1.7536272335648937, 10.898985481755824, 1.8241252890179567], index=1), Atom('Cu', [1.994555230114336,...</td>\n",
- " <td>-343.805462</td>\n",
- " <td>[[0.42819147658911194, -1.3404675072584398, -0.840042992949223], [0.47822853532413195, -0.07949121681390955, -0.36737406099153697], [-0.9172012989939929, 0.3239785862266829, -0.013009375610539234]...</td>\n",
+ " <td>lammps_job_vac</td>\n",
+ " <td>(Atom('Cu', [10.866365841890905, 1.8491297824671196, 2.0932441820713352], index=0), Atom('Cu', [1.7536272327257443, 10.898985480280807, 1.8241252899027136], index=1), Atom('Cu', [1.994555229954278...</td>\n",
+ " <td>-353.959714</td>\n",
+ " <td>[[0.4281914688318719, -1.3404674992878498, -0.840042998895605], [0.4782285361793289, -0.07949121879472046, -0.36737406210665396], [-0.9172013000957939, 0.3239785884620919, -0.013009379648141535], ...</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.0, 1.4737763285740602, 9.024277317236803e-17], index=0), Atom('Cu', [2.5526554800834376, 1.4737763285740604, 2.4654784132287465e-16], index=1), Atom('Cu', [5.105310960166875, 1.4737...</td>\n",
- " <td>-400.111422</td>\n",
+ " <td>lammps_job_surf</td>\n",
+ " <td>(Atom('Cu', [0.0, 1.47377632857406, 9.024277317236802e-17], index=0), Atom('Cu', [2.5526554800834376, 1.4737763285740602, 2.4654784132287465e-16], index=1), Atom('Cu', [5.105310960166875, 1.473776...</td>\n",
+ " <td>-426.377084</td>\n",
" <td>[[-8.770761894538741e-15, 1.15657750043852e-09, 0.109956784695642], [-1.0824674490095302e-15, 1.15657515335765e-09, 0.10995678469564], [2.4308896909297503e-15, 1.1565748914144101e-09, 0.1099567846...</td>\n",
" <td>128.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.4126496185424086, 0.9553642998076769, 0.12158924761653131], index=0), Atom('Cu', [2.831118842513476, 1.0700158216349893, 25.916922304272767], index=1), Atom('Cu', [5.314789299948389...</td>\n",
- " <td>-399.591699</td>\n",
- " <td>[[-0.570999988178131, 1.09833300989089, -0.566585257198732], [-0.0492000252908114, 0.597416506778632, 0.966635869516025], [-0.0024866801464792, -0.0674126479000064, 0.2546322145358], [0.2289085885...</td>\n",
+ " <td>lammps_job_surf</td>\n",
+ " <td>(Atom('Cu', [0.4126496185424086, 0.9553642998076769, 0.12158924761653131], index=0), Atom('Cu', [2.831118842513476, 1.070015821634989, 25.91692230427277], index=1), Atom('Cu', [5.314789299948389, ...</td>\n",
+ " <td>-412.725659</td>\n",
+ " <td>[[-0.570999988178138, 1.09833300989088, -0.566585257198733], [-0.0492000252908119, 0.59741650677863, 0.966635869516026], [-0.0024866801464869, -0.0674126479000064, 0.2546322145358], [0.22890858859...</td>\n",
" <td>128.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.151562776611806, 1.6587733567107183, 26.43035938894712], index=0), Atom('Cu', [2.5212828819853748, 1.9070842851152785, 26.36203940098048], index=1), Atom('Cu', [4.715098279443367, ...</td>\n",
- " <td>-398.422617</td>\n",
- " <td>[[-0.555658749547497, 0.42566314828364993, 0.466167261187443], [-1.39449792316952, -0.5017985009363372, 0.4456531170887249], [1.25410857633035, -0.33111996310077596, -0.24658722383829995], [0.3911...</td>\n",
+ " <td>lammps_job_surf</td>\n",
+ " <td>(Atom('Cu', [10.151562776611796, 1.6587733567107221, 26.430359388947146], index=0), Atom('Cu', [2.521282881985372, 1.9070842851152827, 26.3620394009805], index=1), Atom('Cu', [4.715098279444533, 1...</td>\n",
+ " <td>-412.248744</td>\n",
+ " <td>[[-0.555658749547297, 0.42566314828364293, 0.466167261187242], [-1.39449792316991, -0.5017985009363141, 0.44565311708876987], [1.25410857633047, -0.33111996310053693, -0.24658722383835793], [0.391...</td>\n",
" <td>128.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.19573901040697333, 1.051460534941847, 26.530022925039237], index=0), Atom('Cu', [2.995726956631421, 1.1546751485293076, 26.840478692293868], index=1), Atom('Cu', [5.923947432763936,...</td>\n",
- " <td>-395.110340</td>\n",
- " <td>[[0.550884754551829, 0.33865983742272204, 0.12042096866985205], [0.189727589275692, -0.524367356684999, -0.17886090450862402], [-1.6758650723099, -0.31030261483377614, 0.6946509585213989], [0.4453...</td>\n",
+ " <td>lammps_job_surf</td>\n",
+ " <td>(Atom('Cu', [0.19573901040814495, 1.051460534941852, 26.530022925039262], index=0), Atom('Cu', [2.9957269566314313, 1.1546751485293134, -0.001778040455061588], index=1), Atom('Cu', [5.923947432763...</td>\n",
+ " <td>-408.987597</td>\n",
+ " <td>[[0.550884754551255, 0.33865983742361505, 0.12042096866994706], [0.189727589275785, -0.524367356684469, -0.17886090450817402], [-1.6758650723095, -0.3103026148332811, 0.6946509585210929], [0.44535...</td>\n",
" <td>128.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [9.888379625890167, 1.4968159202602271, 26.58551059472236], index=0), Atom('Cu', [2.207544057015841, 1.290074245861123, 26.685466830386254], index=1), Atom('Cu', [4.939145903748478, 1....</td>\n",
- " <td>-396.833326</td>\n",
- " <td>[[0.235449441284142, 0.601163461325591, 0.04737458109310825], [0.337344266123105, 0.66485729403382, 0.37768738132523805], [-0.171831038530166, -0.11286098093109101, -0.07415135874528352], [-0.1966...</td>\n",
+ " <td>lammps_job_surf</td>\n",
+ " <td>(Atom('Cu', [9.88837962589013, 1.4968159202602165, 26.58551059472219], index=0), Atom('Cu', [2.2075440570158325, 1.2900742458621188, 26.685466830384563], index=1), Atom('Cu', [4.939145903748459, 1...</td>\n",
+ " <td>-410.603331</td>\n",
+ " <td>[[0.235449441283078, 0.601163461328045, 0.047374581092271446], [0.337344266120139, 0.664857294034072, 0.37768738132641905], [-0.171831038532397, -0.11286098092806901, -0.07415135874388401], [-0.19...</td>\n",
" <td>128.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.3429326290376805, 1.5170285327525175, 0.14896315086845416], index=0), Atom('Cu', [3.129345868860857, 1.495102964861245, 0.01607848650845961], index=1), Atom('Cu', [5.714332108258016...</td>\n",
- " <td>-400.444368</td>\n",
- " <td>[[1.15453322332565, 0.04603660924771737, -1.21282260887155], [-0.403390913673288, -0.18969536874868004, 0.030526533198225264], [-0.683024325114862, -0.967333273082708, 0.4114320467737619], [-0.867...</td>\n",
+ " <td>lammps_job_surf</td>\n",
+ " <td>(Atom('Cu', [0.34293262903884986, 1.5170285327525375, 0.1489631508684563], index=0), Atom('Cu', [3.1293458688620657, 1.4951029648602612, 0.016078486508459836], index=1), Atom('Cu', [5.714332108259...</td>\n",
+ " <td>-412.068287</td>\n",
+ " <td>[[1.15453322332514, 0.04603660924936107, -1.21282260886917], [-0.40339091367313, -0.18969536874822704, 0.030526533197075965], [-0.683024325118812, -0.967333273080052, 0.4114320467711059], [-0.8675...</td>\n",
" <td>128.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [9.95801241508363, 1.4359511579313753, 26.639342243648862], index=0), Atom('Cu', [2.2681416259098204, 1.7861270127574713, 0.0041936446239185], index=1), Atom('Cu', [5.074253116948648, ...</td>\n",
- " <td>-397.843043</td>\n",
- " <td>[[0.742004122721361, 0.22577315893502206, 0.12298868177971806], [0.771069504028052, -0.90014541931557, -0.660403448738781], [-0.462456173466872, 1.24796716628589, -0.4192010350460109], [0.08219110...</td>\n",
+ " <td>lammps_job_surf</td>\n",
+ " <td>(Atom('Cu', [9.95801241508353, 1.4359511579323643, 26.639342243648876], index=0), Atom('Cu', [2.2681416259097977, 1.7861270127584556, 0.004193644623918503], index=1), Atom('Cu', [5.074253116948597...</td>\n",
+ " <td>-410.426591</td>\n",
+ " <td>[[0.742004122720721, 0.22577315892753805, 0.12298868178080906], [0.771069504023197, -0.90014541932296, -0.660403448735684], [-0.462456173471762, 1.24796716627208, -0.41920103505081097], [0.0821911...</td>\n",
" <td>128.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.225027496779227, 1.7848555655274232, 26.42388292769549], index=0), Atom('Cu', [2.4914869938632442, 1.861119175102061, 26.64826771541495], index=1), Atom('Cu', [4.968442272624181, 1...</td>\n",
- " <td>-399.776798</td>\n",
- " <td>[[0.552749731038993, -0.46087407707755496, 0.177277816519943], [0.340079509783844, 0.23574679643440802, 0.26784307872713603], [0.388298578408562, 0.331969932822417, 0.34399524632239104], [-0.59826...</td>\n",
+ " <td>lammps_job_surf</td>\n",
+ " <td>(Atom('Cu', [10.225027496778678, 1.7848555655263902, 26.42388292769736], index=0), Atom('Cu', [2.491486993862084, 1.8611191751030531, 26.648267715418335], index=1), Atom('Cu', [4.968442272628866, ...</td>\n",
+ " <td>-413.081270</td>\n",
+ " <td>[[0.552749731039339, -0.46087407706439093, 0.177277816520108], [0.340079509796374, 0.23574679643604202, 0.26784307872275903], [0.38829857840138, 0.331969932800717, 0.34399524631836903], [-0.598269...</td>\n",
" <td>128.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.188116701703345, 1.3482529143763475, 26.353847647582224], index=0), Atom('Cu', [2.4559442807081986, 1.4757747059762696, 26.57379282025388], index=1), Atom('Cu', [5.077086561123585,...</td>\n",
- " <td>-399.168074</td>\n",
- " <td>[[0.570532228156976, 0.17193739345012202, 0.796311675035985], [0.67297011060888, -0.81773224395498, 0.207420342553107], [0.391385096561351, -0.862073619446019, 0.597813478897486], [-0.270452726779...</td>\n",
+ " <td>lammps_job_surf</td>\n",
+ " <td>(Atom('Cu', [10.18811670169521, 1.3482529143794337, 26.353847647583958], index=0), Atom('Cu', [2.4559442807083434, 1.4757747059783504, 26.573792820255626], index=1), Atom('Cu', [5.077086561123885,...</td>\n",
+ " <td>-411.270168</td>\n",
+ " <td>[[0.570532228170616, 0.17193739346638104, 0.796311675023456], [0.672970110578221, -0.817732243959863, 0.207420342553793], [0.391385096556072, -0.862073619432422, 0.597813478903035], [-0.2704527267...</td>\n",
" <td>128.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [10.165259029706933, 1.7122531598094173, 26.335638965295193], index=0), Atom('Cu', [2.3513330869200098, 1.5487334168655387, 26.34258116233103], index=1), Atom('Cu', [4.95573138146394, ...</td>\n",
- " <td>-396.984690</td>\n",
- " <td>[[-1.11093024791963, -0.6986058376048401, 0.44674121624886787], [0.0416051560041239, -0.0424634970331458, -0.0874515615419252], [-0.135122617155289, -0.778975629913174, -1.04402814519501], [0.8442...</td>\n",
+ " <td>lammps_job_surf</td>\n",
+ " <td>(Atom('Cu', [10.165259029717141, 1.7122531597864588, 26.335638965282506], index=0), Atom('Cu', [2.351333086964575, 1.5487334167881261, 26.342581162362286], index=1), Atom('Cu', [4.955731381487939,...</td>\n",
+ " <td>-410.951862</td>\n",
+ " <td>[[-1.11093024775332, -0.6986058375434832, 0.4467412161766239], [0.0416051560885089, -0.0424634968511256, -0.0874515614131588], [-0.135122617107614, -0.778975629931571, -1.04402814517475], [0.84425...</td>\n",
" <td>128.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
- " <td>None</td>\n",
- " <td>(Atom('Cu', [0.36893784059756035, 1.481831927531004, 26.53504700964859], index=0), Atom('Cu', [2.6965712181950883, 1.488812429574298, 26.730123522873537], index=1), Atom('Cu', [5.668661292604278, ...</td>\n",
- " <td>-399.581912</td>\n",
- " <td>[[-0.645092069902244, 0.04920122139904896, 0.21081031345366397], [1.14893686034324, -0.6114894792387109, -0.40665475671972495], [-0.287092609914331, 0.292443458702414, -0.801010316546939], [-0.184...</td>\n",
+ " <td>lammps_job_surf</td>\n",
+ " <td>(Atom('Cu', [0.3689378411745667, 1.4818319280204475, 26.535047009582307], index=0), Atom('Cu', [2.6965712182140265, 1.4888124298358478, 26.730123523791978], index=1), Atom('Cu', [5.668661293887029...</td>\n",
+ " <td>-411.163952</td>\n",
+ " <td>[[-0.645092069842764, 0.049201221165188956, 0.21081031392484997], [1.14893686212593, -0.6114894802487358, -0.40665475714370897], [-0.287092610235497, 0.292443460035198, -0.801010315713759], [-0.18...</td>\n",
" <td>128.0</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -1466,47 +939,47 @@
"</div>"
],
"text/plain": [
- " name \\\n",
- "0 None \n",
- "1 None \n",
- "2 None \n",
- "3 None \n",
- "4 None \n",
- "5 None \n",
- "6 None \n",
- "7 None \n",
- "8 None \n",
- "9 None \n",
- "10 None \n",
- "11 None \n",
- "12 None \n",
- "13 None \n",
- "14 None \n",
- "15 None \n",
- "16 None \n",
- "17 None \n",
- "18 None \n",
- "19 None \n",
- "20 None \n",
- "21 None \n",
- "22 None \n",
- "23 None \n",
- "24 None \n",
- "25 None \n",
- "26 None \n",
- "27 None \n",
- "28 None \n",
- "29 None \n",
- "30 None \n",
- "31 None \n",
- "32 None \n",
- "33 None \n",
- "34 None \n",
- "35 None \n",
- "36 None \n",
- "37 None \n",
- "38 None \n",
- "39 None \n",
+ " name \\\n",
+ "0 job_a_3_4 \n",
+ "1 job_a_3_5 \n",
+ "2 job_a_3_6 \n",
+ "3 job_a_3_7 \n",
+ "4 job_a_3_8 \n",
+ "5 job_a_3_9 \n",
+ "6 job_a_4_0 \n",
+ "7 lammps_job \n",
+ "8 lammps_job \n",
+ "9 lammps_job \n",
+ "10 lammps_job \n",
+ "11 lammps_job \n",
+ "12 lammps_job \n",
+ "13 lammps_job \n",
+ "14 lammps_job \n",
+ "15 lammps_job \n",
+ "16 lammps_job \n",
+ "17 lammps_job \n",
+ "18 lammps_job_vac \n",
+ "19 lammps_job_vac \n",
+ "20 lammps_job_vac \n",
+ "21 lammps_job_vac \n",
+ "22 lammps_job_vac \n",
+ "23 lammps_job_vac \n",
+ "24 lammps_job_vac \n",
+ "25 lammps_job_vac \n",
+ "26 lammps_job_vac \n",
+ "27 lammps_job_vac \n",
+ "28 lammps_job_vac \n",
+ "29 lammps_job_surf \n",
+ "30 lammps_job_surf \n",
+ "31 lammps_job_surf \n",
+ "32 lammps_job_surf \n",
+ "33 lammps_job_surf \n",
+ "34 lammps_job_surf \n",
+ "35 lammps_job_surf \n",
+ "36 lammps_job_surf \n",
+ "37 lammps_job_surf \n",
+ "38 lammps_job_surf \n",
+ "39 lammps_job_surf \n",
"\n",
" atoms \\\n",
"0 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
@@ -1516,39 +989,39 @@
"4 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
"5 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
"6 (Atom('Cu', [0.0, 0.0, 0.0], index=0)) \n",
- "7 (Atom('Cu', [0.0, 0.0, 0.0], index=0), Atom('Cu', [0.0, 1.8049999999995918, 1.804999999999592], index=1), Atom('Cu', [1.8049999999995918, 1.1052437362302365e-16, 1.804999999999592], index=2), Atom... \n",
- "8 (Atom('Cu', [0.140426153531212, 11.00934611760493, 10.968207696001379], index=0), Atom('Cu', [10.983357200359302, 1.779939365335074, 1.7146804782560905], index=1), Atom('Cu', [2.1228644677344763, ... \n",
- "9 (Atom('Cu', [0.1407579358923329, 11.020287239626356, 10.855878337455094], index=0), Atom('Cu', [0.29542104007972325, 1.6514729828183248, 1.7760939949715002], index=1), Atom('Cu', [1.99578258187561... \n",
- "10 (Atom('Cu', [0.04675277832683521, 10.951786003502875, 0.20014601215416555], index=0), Atom('Cu', [0.08453269827616121, 1.807218974303731, 1.7664733621606055], index=1), Atom('Cu', [1.9597357123378... \n",
- "11 (Atom('Cu', [10.991332780892245, 11.027168829788469, 0.17044757336885022], index=0), Atom('Cu', [10.869778028731718, 1.9053524080340238, 1.7569642307109068], index=1), Atom('Cu', [1.89027128290555... \n",
- "12 (Atom('Cu', [10.958070767872973, 0.05270018288525903, 11.015795828313184], index=0), Atom('Cu', [0.04540970504125744, 2.0780519854689388, 1.8353871076994486], index=1), Atom('Cu', [1.8285627799497... \n",
- "13 (Atom('Cu', [10.903697402270481, 10.843249424147054, 0.09574490333935876], index=0), Atom('Cu', [10.594918916668115, 1.7187193807013557, 1.957301113531466], index=1), Atom('Cu', [1.676074002401865... \n",
- "14 (Atom('Cu', [0.25841198959969514, 0.07063449316617948, 10.912283030401635], index=0), Atom('Cu', [0.19649020014428978, 1.7891621064498946, 1.803181486480956], index=1), Atom('Cu', [1.7493171890008... \n",
- "15 (Atom('Cu', [0.004994793789590358, 10.985055637636528, 10.904222762438215], index=0), Atom('Cu', [10.941729664004177, 1.8868614122932958, 1.8147512571785023], index=1), Atom('Cu', [1.8438031255437... \n",
- "16 (Atom('Cu', [0.03645923830333788, 10.856010693932728, 10.970572990196315], index=0), Atom('Cu', [10.913522535008392, 1.8161914381204696, 1.5447581077411967], index=1), Atom('Cu', [1.77583933019795... \n",
- "17 (Atom('Cu', [0.009767181825404037, 0.0071865231528328395, 11.0089023992238], index=0), Atom('Cu', [10.89374596605574, 1.9969194212313586, 1.595578837066423], index=1), Atom('Cu', [2.05442192913884... \n",
- "18 (Atom('Cu', [0.0, 1.8049999999995918, 1.804999999999592], index=0), Atom('Cu', [1.8049999999995918, 1.1052437362302365e-16, 1.804999999999592], index=1), Atom('Cu', [1.8049999999995918, 1.80499999... \n",
- "19 (Atom('Cu', [0.10390109091492504, 1.9580560553035629, 1.779188560767685], index=0), Atom('Cu', [1.3868099266682106, 10.93717630776342, 1.601790948781396], index=1), Atom('Cu', [1.7183987889409966,... \n",
- "20 (Atom('Cu', [0.2223234793002756, 1.6999920633404135, 1.7623980235259114], index=0), Atom('Cu', [2.122882660879041, 0.18624238423025943, 2.030808683988878], index=1), Atom('Cu', [1.7334689484700414... \n",
- "21 (Atom('Cu', [10.839434974930338, 1.9008464900018593, 1.6867963294684278], index=0), Atom('Cu', [1.5876598581358559, 0.15447557118789498, 1.715299994817798], index=1), Atom('Cu', [2.148606011735498... \n",
- "22 (Atom('Cu', [10.827044041159013, 1.814885520198166, 2.1241909251750846], index=0), Atom('Cu', [1.6679142700199647, 0.1106240807045643, 1.629401459425781], index=1), Atom('Cu', [1.29418686613977, 2... \n",
- "23 (Atom('Cu', [0.18579648605077317, 1.6025971043596021, 1.8725196193757527], index=0), Atom('Cu', [2.0480835165906024, 0.055238510607153124, 2.258841094259334], index=1), Atom('Cu', [1.8423446841272... \n",
- "24 (Atom('Cu', [10.902653792490328, 1.9016481867720487, 1.712386801004505], index=0), Atom('Cu', [1.6843656295425262, 0.20454959542790652, 1.4940727709027473], index=1), Atom('Cu', [1.628967729295861... \n",
- "25 (Atom('Cu', [0.2202511036922136, 1.5872348575607442, 1.7623998700282655], index=0), Atom('Cu', [1.8386689358546338, 10.988833155546024, 1.8571406735818983], index=1), Atom('Cu', [1.976390891645592... \n",
- "26 (Atom('Cu', [10.746144724861097, 1.6561216261540705, 1.6676063346982197], index=0), Atom('Cu', [1.8697696006277247, 0.29566102043388925, 1.9071090181664805], index=1), Atom('Cu', [1.82013088394316... \n",
- "27 (Atom('Cu', [0.17063059290766686, 1.8534187978406447, 1.7423748775395635], index=0), Atom('Cu', [1.8595651370150335, 10.90572322929213, 1.810598025463111], index=1), Atom('Cu', [1.934651223968069,... \n",
- "28 (Atom('Cu', [10.866365842640102, 1.8491297818376022, 2.093244182315977], index=0), Atom('Cu', [1.7536272335648937, 10.898985481755824, 1.8241252890179567], index=1), Atom('Cu', [1.994555230114336,... \n",
- "29 (Atom('Cu', [0.0, 1.4737763285740602, 9.024277317236803e-17], index=0), Atom('Cu', [2.5526554800834376, 1.4737763285740604, 2.4654784132287465e-16], index=1), Atom('Cu', [5.105310960166875, 1.4737... \n",
- "30 (Atom('Cu', [0.4126496185424086, 0.9553642998076769, 0.12158924761653131], index=0), Atom('Cu', [2.831118842513476, 1.0700158216349893, 25.916922304272767], index=1), Atom('Cu', [5.314789299948389... \n",
- "31 (Atom('Cu', [10.151562776611806, 1.6587733567107183, 26.43035938894712], index=0), Atom('Cu', [2.5212828819853748, 1.9070842851152785, 26.36203940098048], index=1), Atom('Cu', [4.715098279443367, ... \n",
- "32 (Atom('Cu', [0.19573901040697333, 1.051460534941847, 26.530022925039237], index=0), Atom('Cu', [2.995726956631421, 1.1546751485293076, 26.840478692293868], index=1), Atom('Cu', [5.923947432763936,... \n",
- "33 (Atom('Cu', [9.888379625890167, 1.4968159202602271, 26.58551059472236], index=0), Atom('Cu', [2.207544057015841, 1.290074245861123, 26.685466830386254], index=1), Atom('Cu', [4.939145903748478, 1.... \n",
- "34 (Atom('Cu', [0.3429326290376805, 1.5170285327525175, 0.14896315086845416], index=0), Atom('Cu', [3.129345868860857, 1.495102964861245, 0.01607848650845961], index=1), Atom('Cu', [5.714332108258016... \n",
- "35 (Atom('Cu', [9.95801241508363, 1.4359511579313753, 26.639342243648862], index=0), Atom('Cu', [2.2681416259098204, 1.7861270127574713, 0.0041936446239185], index=1), Atom('Cu', [5.074253116948648, ... \n",
- "36 (Atom('Cu', [10.225027496779227, 1.7848555655274232, 26.42388292769549], index=0), Atom('Cu', [2.4914869938632442, 1.861119175102061, 26.64826771541495], index=1), Atom('Cu', [4.968442272624181, 1... \n",
- "37 (Atom('Cu', [10.188116701703345, 1.3482529143763475, 26.353847647582224], index=0), Atom('Cu', [2.4559442807081986, 1.4757747059762696, 26.57379282025388], index=1), Atom('Cu', [5.077086561123585,... \n",
- "38 (Atom('Cu', [10.165259029706933, 1.7122531598094173, 26.335638965295193], index=0), Atom('Cu', [2.3513330869200098, 1.5487334168655387, 26.34258116233103], index=1), Atom('Cu', [4.95573138146394, ... \n",
- "39 (Atom('Cu', [0.36893784059756035, 1.481831927531004, 26.53504700964859], index=0), Atom('Cu', [2.6965712181950883, 1.488812429574298, 26.730123522873537], index=1), Atom('Cu', [5.668661292604278, ... \n",
+ "7 (Atom('Cu', [0.0, 0.0, 0.0], index=0), Atom('Cu', [0.0, 1.804999999999592, 1.804999999999592], index=1), Atom('Cu', [1.804999999999592, 1.1052437362302367e-16, 1.804999999999592], index=2), Atom('... \n",
+ "8 (Atom('Cu', [0.140426153531212, 11.00934611760493, 10.96820769600138], index=0), Atom('Cu', [10.983357200359302, 1.779939365335074, 1.7146804782560903], index=1), Atom('Cu', [2.1228644677344763, 0... \n",
+ "9 (Atom('Cu', [0.1407579358923329, 11.020287239626356, 10.855878337455094], index=0), Atom('Cu', [0.29542104007972325, 1.6514729828183248, 1.7760939949715], index=1), Atom('Cu', [1.9957825818756145,... \n",
+ "10 (Atom('Cu', [0.046752778326835207, 10.951786003502875, 0.20014601215416555], index=0), Atom('Cu', [0.08453269827616121, 1.8072189743037306, 1.7664733621606052], index=1), Atom('Cu', [1.95973571233... \n",
+ "11 (Atom('Cu', [10.99133278089223, 11.027168829788456, 0.17044757336885], index=0), Atom('Cu', [10.869778028731703, 1.9053524080340212, 1.7569642307109046], index=1), Atom('Cu', [1.8902712829055568, ... \n",
+ "12 (Atom('Cu', [10.958070767873476, 0.05270018288526145, 11.015795828313692], index=0), Atom('Cu', [0.045409705041259525, 2.078051985469034, 1.8353871076995325], index=1), Atom('Cu', [1.8285627799498... \n",
+ "13 (Atom('Cu', [10.903697402270652, 10.843249424146602, 0.09574490333936027], index=0), Atom('Cu', [10.59491891666766, 1.7187193807026324, 1.9573011135302476], index=1), Atom('Cu', [1.676074002401891... \n",
+ "14 (Atom('Cu', [0.258411989603464, 0.0706344931649224, 10.912283030397811], index=0), Atom('Cu', [0.1964902001455455, 1.7891621064461172, 1.8031814864809468], index=1), Atom('Cu', [1.7493171890021084... \n",
+ "15 (Atom('Cu', [0.004994793782076363, 10.985055637641707, 10.904222762435255], index=0), Atom('Cu', [10.9417296640056, 1.886861412294462, 1.8147512571859394], index=1), Atom('Cu', [1.8438031255436442... \n",
+ "16 (Atom('Cu', [0.03645923831331586, 10.8560106939291, 10.97057299019823], index=0), Atom('Cu', [10.913522535013476, 1.816191438106623, 1.5447581077438086], index=1), Atom('Cu', [1.7758393302091413, ... \n",
+ "17 (Atom('Cu', [0.00976718182041884, 0.007186523165377699, 11.008902399259645], index=0), Atom('Cu', [10.89374596607187, 1.9969194212527828, 1.5955788370755422], index=1), Atom('Cu', [2.0544219291027... \n",
+ "18 (Atom('Cu', [0.0, 1.804999999999592, 1.804999999999592], index=0), Atom('Cu', [1.804999999999592, 1.1052437362302367e-16, 1.804999999999592], index=1), Atom('Cu', [1.804999999999592, 1.80499999999... \n",
+ "19 (Atom('Cu', [0.10390109091492468, 1.9580560553035562, 1.7791885607676787], index=0), Atom('Cu', [1.3868099266682057, 10.93717630776338, 1.6017909487813904], index=1), Atom('Cu', [1.718398788940990... \n",
+ "20 (Atom('Cu', [0.222323479300276, 1.6999920633404162, 1.7623980235259142], index=0), Atom('Cu', [2.1228826608790445, 0.1862423842302597, 2.0308086839888815], index=1), Atom('Cu', [1.733468948470044,... \n",
+ "21 (Atom('Cu', [10.839434974930233, 1.9008464900018414, 1.6867963294684114], index=0), Atom('Cu', [1.587659858135841, 0.1544755711878935, 1.7152999948177818], index=1), Atom('Cu', [2.148606011735478,... \n",
+ "22 (Atom('Cu', [10.827044041158409, 1.8148855201981695, 2.124190925175089], index=0), Atom('Cu', [1.6679142700199683, 0.11062408070456453, 1.6294014594257846], index=1), Atom('Cu', [1.294186866136013... \n",
+ "23 (Atom('Cu', [0.18579648603934987, 1.6025971043768155, 1.8725196193702205], index=0), Atom('Cu', [2.048083516582408, 0.05523851059960332, 2.2588410942434316], index=1), Atom('Cu', [1.84234468412923... \n",
+ "24 (Atom('Cu', [10.902653792488254, 1.9016481867624628, 1.7123868009764087], index=0), Atom('Cu', [1.684365629566867, 0.20454959543267073, 1.4940727709048371], index=1), Atom('Cu', [1.628967729292809... \n",
+ "25 (Atom('Cu', [0.22025110375749696, 1.5872348575731654, 1.7623998700921586], index=0), Atom('Cu', [1.8386689358494586, 10.988833155464064, 1.8571406735076377], index=1), Atom('Cu', [1.97639089167179... \n",
+ "26 (Atom('Cu', [10.746144724792629, 1.6561216261325158, 1.6676063346541934], index=0), Atom('Cu', [1.8697696007010116, 0.2956610204486699, 1.9071090181785917], index=1), Atom('Cu', [1.820130883905895... \n",
+ "27 (Atom('Cu', [0.17063059334564, 1.853418797177199, 1.7423748774412284], index=0), Atom('Cu', [1.8595651381460672, 10.905723229638589, 1.810598025743776], index=1), Atom('Cu', [1.9346512242265819, 1... \n",
+ "28 (Atom('Cu', [10.866365841890905, 1.8491297824671196, 2.0932441820713352], index=0), Atom('Cu', [1.7536272327257443, 10.898985480280807, 1.8241252899027136], index=1), Atom('Cu', [1.994555229954278... \n",
+ "29 (Atom('Cu', [0.0, 1.47377632857406, 9.024277317236802e-17], index=0), Atom('Cu', [2.5526554800834376, 1.4737763285740602, 2.4654784132287465e-16], index=1), Atom('Cu', [5.105310960166875, 1.473776... \n",
+ "30 (Atom('Cu', [0.4126496185424086, 0.9553642998076769, 0.12158924761653131], index=0), Atom('Cu', [2.831118842513476, 1.070015821634989, 25.91692230427277], index=1), Atom('Cu', [5.314789299948389, ... \n",
+ "31 (Atom('Cu', [10.151562776611796, 1.6587733567107221, 26.430359388947146], index=0), Atom('Cu', [2.521282881985372, 1.9070842851152827, 26.3620394009805], index=1), Atom('Cu', [4.715098279444533, 1... \n",
+ "32 (Atom('Cu', [0.19573901040814495, 1.051460534941852, 26.530022925039262], index=0), Atom('Cu', [2.9957269566314313, 1.1546751485293134, -0.001778040455061588], index=1), Atom('Cu', [5.923947432763... \n",
+ "33 (Atom('Cu', [9.88837962589013, 1.4968159202602165, 26.58551059472219], index=0), Atom('Cu', [2.2075440570158325, 1.2900742458621188, 26.685466830384563], index=1), Atom('Cu', [4.939145903748459, 1... \n",
+ "34 (Atom('Cu', [0.34293262903884986, 1.5170285327525375, 0.1489631508684563], index=0), Atom('Cu', [3.1293458688620657, 1.4951029648602612, 0.016078486508459836], index=1), Atom('Cu', [5.714332108259... \n",
+ "35 (Atom('Cu', [9.95801241508353, 1.4359511579323643, 26.639342243648876], index=0), Atom('Cu', [2.2681416259097977, 1.7861270127584556, 0.004193644623918503], index=1), Atom('Cu', [5.074253116948597... \n",
+ "36 (Atom('Cu', [10.225027496778678, 1.7848555655263902, 26.42388292769736], index=0), Atom('Cu', [2.491486993862084, 1.8611191751030531, 26.648267715418335], index=1), Atom('Cu', [4.968442272628866, ... \n",
+ "37 (Atom('Cu', [10.18811670169521, 1.3482529143794337, 26.353847647583958], index=0), Atom('Cu', [2.4559442807083434, 1.4757747059783504, 26.573792820255626], index=1), Atom('Cu', [5.077086561123885,... \n",
+ "38 (Atom('Cu', [10.165259029717141, 1.7122531597864588, 26.335638965282506], index=0), Atom('Cu', [2.351333086964575, 1.5487334167881261, 26.342581162362286], index=1), Atom('Cu', [4.955731381487939,... \n",
+ "39 (Atom('Cu', [0.3689378411745667, 1.4818319280204475, 26.535047009582307], index=0), Atom('Cu', [2.6965712182140265, 1.4888124298358478, 26.730123523791978], index=1), Atom('Cu', [5.668661293887029... \n",
"\n",
" energy \\\n",
"0 -3.142019 \n",
@@ -1558,39 +1031,39 @@
"4 -3.330215 \n",
"5 -3.195118 \n",
"6 -3.035358 \n",
- "7 -347.182406 \n",
- "8 -348.253665 \n",
- "9 -345.424528 \n",
- "10 -346.758349 \n",
- "11 -344.603627 \n",
- "12 -346.849801 \n",
- "13 -345.015235 \n",
- "14 -346.569097 \n",
- "15 -344.892954 \n",
- "16 -347.628843 \n",
- "17 -345.776979 \n",
- "18 -342.906251 \n",
- "19 -343.569002 \n",
- "20 -343.317892 \n",
- "21 -341.057270 \n",
- "22 -342.067006 \n",
- "23 -342.528496 \n",
- "24 -342.900954 \n",
- "25 -340.983380 \n",
- "26 -342.479965 \n",
- "27 -340.174298 \n",
- "28 -343.805462 \n",
- "29 -400.111422 \n",
- "30 -399.591699 \n",
- "31 -398.422617 \n",
- "32 -395.110340 \n",
- "33 -396.833326 \n",
- "34 -400.444368 \n",
- "35 -397.843043 \n",
- "36 -399.776798 \n",
- "37 -399.168074 \n",
- "38 -396.984690 \n",
- "39 -399.581912 \n",
+ "7 -369.311743 \n",
+ "8 -360.190839 \n",
+ "9 -356.403521 \n",
+ "10 -358.245754 \n",
+ "11 -356.564325 \n",
+ "12 -357.011799 \n",
+ "13 -357.856759 \n",
+ "14 -358.316140 \n",
+ "15 -356.847816 \n",
+ "16 -358.626624 \n",
+ "17 -356.796173 \n",
+ "18 -364.828772 \n",
+ "19 -353.817356 \n",
+ "20 -353.115830 \n",
+ "21 -352.375089 \n",
+ "22 -352.774500 \n",
+ "23 -352.466296 \n",
+ "24 -353.659518 \n",
+ "25 -352.032412 \n",
+ "26 -352.885609 \n",
+ "27 -352.295316 \n",
+ "28 -353.959714 \n",
+ "29 -426.377084 \n",
+ "30 -412.725659 \n",
+ "31 -412.248744 \n",
+ "32 -408.987597 \n",
+ "33 -410.603331 \n",
+ "34 -412.068287 \n",
+ "35 -410.426591 \n",
+ "36 -413.081270 \n",
+ "37 -411.270168 \n",
+ "38 -410.951862 \n",
+ "39 -411.163952 \n",
"\n",
" forces \\\n",
"0 [[1.1869253621046177e-16, -1.7429070520896771e-16, -1.397277865277868e-16]] \n",
@@ -1602,37 +1075,37 @@
"6 [[-5.946777565406637e-17, -1.0605082175909553e-16, -1.2946304704347008e-16]] \n",
"7 [[-1.2656542480726799e-14, -1.46965772884755e-14, -1.61017033040167e-14], [-1.3905543383430098e-14, 4.5310977192514186e-15, 4.8333732849403796e-15], [4.9682480351975795e-15, -1.4072076837123899e-1... \n",
"8 [[-0.21910202935187897, -0.37573419410584397, 0.43392575377979187], [0.16208168404695897, -0.00671505675904709, 1.03458554920361], [-1.2001630139266497, -0.40207322348963503, -0.45620473735655703]... \n",
- "9 [[-0.023031834879881696, 0.042841438691562095, 0.5899774836434479], [-0.5418151518759569, 0.6754733604036028, -0.5582999589284809], [-0.6011411771360858, -0.355590065329821, -0.0035901986306415582... \n",
- "10 [[-0.32237334386615796, 0.43406651671724894, -0.5886238546572939], [-0.6295919499998729, 0.07530471384300086, -0.12687342568230403], [-0.06506588733991228, 0.8782024477953109, -0.12243680387297393... \n",
- "11 [[0.45078377546738896, -0.7167806257867769, -0.320969733282763], [0.5707773027838049, -0.5494069530705199, 0.510256621543289], [-0.36439749359274193, 0.17709586752044496, 0.23127352998569298], [0.... \n",
- "12 [[-0.38409855462219894, 0.12077975249818296, 0.024465771201292483], [-0.6560764999451358, -1.05845756801878, -0.658182913095082], [-0.5103420261962529, -0.35656271154938, 0.7090309519821769], [-0.... \n",
- "13 [[0.22044015995668098, -0.2607278388818459, -0.3855032322190499], [0.7754186544660099, -0.04652340102386694, -0.26232164996063695], [0.43147792874898694, -0.9840256256911009, 0.34987911571105595],... \n",
- "14 [[-0.5020206209676749, 0.007307596544171969, 0.24586848661916993], [-1.1645196400331899, 0.1476285180684419, 0.6168850904586589], [0.7183114431792579, -0.5420093171036479, -0.06687387962480258], [... \n",
- "15 [[0.04806202889719739, 0.48969724353819394, 1.29371057615331], [0.5538169933185199, -0.7855310261714289, -0.033081946412792815], [0.34832740609982993, 0.9937361742308158, 0.30650548838004904], [-0... \n",
- "16 [[0.24033044872062098, 1.5305792677179897, -0.6791236163119347], [0.05903491332573559, 0.148151392595253, 0.542468964409148], [0.07921843405670739, 0.145966157214324, 0.7342269238916159], [0.77503... \n",
- "17 [[-0.13926025211222698, -0.0920021629424733, 0.21001691285267599], [0.6767944835284789, -1.1148612203782198, 1.7165284718417098], [-0.7950973845960859, 0.2356681337270629, 0.13328556064538893], [0... \n",
+ "9 [[-0.023031834879864897, 0.04284143869144259, 0.5899774836434099], [-0.5418151518758109, 0.6754733604037649, -0.5582999589285099], [-0.6011411771363389, -0.355590065330048, -0.003590198630652358],... \n",
+ "10 [[-0.32237334386617594, 0.43406651671772695, -0.5886238546573999], [-0.6295919499999019, 0.07530471384292876, -0.12687342568255203], [-0.06506588733974998, 0.8782024477947719, -0.12243680387296693... \n",
+ "11 [[0.45078377546780296, -0.7167806257868728, -0.320969733281809], [0.5707773027839779, -0.5494069530720159, 0.510256621543522], [-0.36439749359319995, 0.17709586752113193, 0.23127352998529296], [0.... \n",
+ "12 [[-0.38409855462373593, 0.12077975249587695, 0.02446577119927368], [-0.6560764999470009, -1.05845756801682, -0.658182913092867], [-0.5103420261931418, -0.356562711546522, 0.7090309519858399], [-0.... \n",
+ "13 [[0.22044015996419397, -0.26072783887691897, -0.38550323222201893], [0.7754186544628359, -0.04652340102588245, -0.2623216499598349], [0.4314779287479719, -0.9840256256937749, 0.34987911571166197],... \n",
+ "14 [[-0.5020206209738959, 0.007307596546447969, 0.24586848662953095], [-1.1645196400318298, 0.14762851807991492, 0.6168850904682569], [0.7183114431738189, -0.5420093170980669, -0.06687387962120929], ... \n",
+ "15 [[0.04806202890911529, 0.4896972435411299, 1.29371057618316], [0.5538169933312109, -0.7855310261686719, -0.033081946439222513], [0.34832740608971496, 0.9937361742507679, 0.30650548838579206], [-0.... \n",
+ "16 [[0.24033044872932896, 1.5305792676779497, -0.6791236163998687], [0.05903491330127059, 0.148151392634253, 0.542468964385662], [0.07921843405774338, 0.145966157230826, 0.7342269239659519], [0.77503... \n",
+ "17 [[-0.13926025200196399, -0.0920021630565197, 0.21001691279918497], [0.6767944835632749, -1.1148612202170798, 1.7165284720013199], [-0.7950973844667609, 0.2356681335380539, 0.13328556065954994], [0... \n",
"18 [[-1.3933298959045699e-14, -0.10827842091784899, -0.108278420917849], [-0.10827842091784899, -1.3877479054990188e-14, -0.108278420917849], [-0.10827842091784899, -0.108278420917849, -1.38424758326... \n",
- "19 [[-0.45079778130218695, 0.19236747741170795, 0.23614131969524693], [1.2984317307784499, 0.04751568933291938, 0.005443425577977482], [0.48875760189522893, -0.5765163470127929, -0.5531364786691719],... \n",
- "20 [[-0.6920262907044069, 0.3986975029533409, 0.10738641941643497], [-1.2097205794553298, -2.21423543020926, -0.8363385508172672], [-0.36839592956658596, 0.35919893904268096, 0.09738195090664269], [2... \n",
- "21 [[0.5607305368018309, 0.07454461225822963, 0.17092757180402804], [0.7460358156721709, -0.9693709228665989, -0.357775642488428], [-1.4982109077380097, 1.0695224870660298, 0.17016025464137993], [1.1... \n",
- "22 [[0.7768634672312369, 0.615614277472231, -0.6520778674544528], [0.5196497874591819, -0.7614150189249069, 0.33452284258026094], [1.0185593798470798, 0.11349094338622906, -1.1138567887728799], [0.39... \n",
- "23 [[-1.2511641505681497, 0.045660252990561916, 0.39426999337691987], [-0.4923847861277789, -0.676187288680257, -1.3802167704923], [-0.12072100498159098, 0.5666924914439229, -0.8825937576849239], [0.... \n",
- "24 [[0.5080309295986539, -0.4875222169174689, 0.46898176405272696], [0.11031309252296198, -0.7243436014688799, 1.2808414836059598], [0.6855285827079979, 0.576817447055947, 0.31599931473888204], [0.50... \n",
- "25 [[-0.6923371397061229, 0.9615117892959398, 0.396261839433264], [0.18083100499512797, -0.47103552335731996, 0.5072798013711759], [-0.22735248831930896, 0.37011177620569996, -1.1740345740817597], [0... \n",
- "26 [[0.46940172398488794, 0.557542533896084, 0.861383405261689], [-0.16043498489103097, -1.0997839627759998, -0.013974118313924476], [-0.13554483762266598, 0.6363641149062679, 0.153788969879775], [1.... \n",
- "27 [[-1.1653987834207897, -0.07515193371140247, -0.13414295102360305], [0.17707911646472896, 0.37588362216028093, 0.10312993516925903], [0.6003632691780849, 0.3869695806628, 0.03431754153949555], [-0... \n",
- "28 [[0.42819147658911194, -1.3404675072584398, -0.840042992949223], [0.47822853532413195, -0.07949121681390955, -0.36737406099153697], [-0.9172012989939929, 0.3239785862266829, -0.013009375610539234]... \n",
+ "19 [[-0.45079778130212095, 0.19236747741186794, 0.23614131969518395], [1.2984317307785798, 0.047515689332818876, 0.0054434255778954815], [0.48875760189529993, -0.5765163470127389, -0.5531364786691438... \n",
+ "20 [[-0.6920262907037699, 0.3986975029534239, 0.10738641941633598], [-1.2097205794551897, -2.2142354302088, -0.8363385508175151], [-0.36839592956672496, 0.3591989390425319, 0.09738195090699889], [2.1... \n",
+ "21 [[0.5607305368014349, 0.07454461225798582, 0.17092757180355503], [0.7460358156722969, -0.9693709228667519, -0.357775642489466], [-1.4982109077379697, 1.0695224870646098, 0.17016025464195195], [1.1... \n",
+ "22 [[0.7768634672242999, 0.615614277463985, -0.6520778674493769], [0.5196497874591279, -0.7614150189291738, 0.33452284258293197], [1.0185593798557397, 0.11349094339204105, -1.1138567887771997], [0.39... \n",
+ "23 [[-1.2511641504900297, 0.045660252979352216, 0.3942699933548249], [-0.49238478619866294, -0.676187288648693, -1.38021677052805], [-0.12072100498243699, 0.5666924914313989, -0.8825937576412469], [0... \n",
+ "24 [[0.5080309295530009, -0.4875222168854389, 0.46898176419400794], [0.11031309245877298, -0.7243436015235899, 1.2808414835860897], [0.6855285827566528, 0.576817447011234, 0.31599931467165404], [0.50... \n",
+ "25 [[-0.6923371397932379, 0.9615117891395759, 0.396261839405581], [0.18083100479582398, -0.47103552318232994, 0.5072798015385259], [-0.22735248852514198, 0.3701117760834369, -1.1740345739673297], [0.... \n",
+ "26 [[0.4694017239681809, 0.557542533620799, 0.861383405389437], [-0.16043498520491098, -1.0997839630796997, -0.013974118048379877], [-0.13554483753545396, 0.6363641150344139, 0.153788969504449], [1.0... \n",
+ "27 [[-1.1653987878668197, -0.07515193358861387, -0.13414295175130306], [0.17707911415253696, 0.37588361844850293, 0.10312993216846503], [0.6003632668071709, 0.386969577632806, 0.03431754138154845], [... \n",
+ "28 [[0.4281914688318719, -1.3404674992878498, -0.840042998895605], [0.4782285361793289, -0.07949121879472046, -0.36737406210665396], [-0.9172013000957939, 0.3239785884620919, -0.013009379648141535], ... \n",
"29 [[-8.770761894538741e-15, 1.15657750043852e-09, 0.109956784695642], [-1.0824674490095302e-15, 1.15657515335765e-09, 0.10995678469564], [2.4308896909297503e-15, 1.1565748914144101e-09, 0.1099567846... \n",
- "30 [[-0.570999988178131, 1.09833300989089, -0.566585257198732], [-0.0492000252908114, 0.597416506778632, 0.966635869516025], [-0.0024866801464792, -0.0674126479000064, 0.2546322145358], [0.2289085885... \n",
- "31 [[-0.555658749547497, 0.42566314828364993, 0.466167261187443], [-1.39449792316952, -0.5017985009363372, 0.4456531170887249], [1.25410857633035, -0.33111996310077596, -0.24658722383829995], [0.3911... \n",
- "32 [[0.550884754551829, 0.33865983742272204, 0.12042096866985205], [0.189727589275692, -0.524367356684999, -0.17886090450862402], [-1.6758650723099, -0.31030261483377614, 0.6946509585213989], [0.4453... \n",
- "33 [[0.235449441284142, 0.601163461325591, 0.04737458109310825], [0.337344266123105, 0.66485729403382, 0.37768738132523805], [-0.171831038530166, -0.11286098093109101, -0.07415135874528352], [-0.1966... \n",
- "34 [[1.15453322332565, 0.04603660924771737, -1.21282260887155], [-0.403390913673288, -0.18969536874868004, 0.030526533198225264], [-0.683024325114862, -0.967333273082708, 0.4114320467737619], [-0.867... \n",
- "35 [[0.742004122721361, 0.22577315893502206, 0.12298868177971806], [0.771069504028052, -0.90014541931557, -0.660403448738781], [-0.462456173466872, 1.24796716628589, -0.4192010350460109], [0.08219110... \n",
- "36 [[0.552749731038993, -0.46087407707755496, 0.177277816519943], [0.340079509783844, 0.23574679643440802, 0.26784307872713603], [0.388298578408562, 0.331969932822417, 0.34399524632239104], [-0.59826... \n",
- "37 [[0.570532228156976, 0.17193739345012202, 0.796311675035985], [0.67297011060888, -0.81773224395498, 0.207420342553107], [0.391385096561351, -0.862073619446019, 0.597813478897486], [-0.270452726779... \n",
- "38 [[-1.11093024791963, -0.6986058376048401, 0.44674121624886787], [0.0416051560041239, -0.0424634970331458, -0.0874515615419252], [-0.135122617155289, -0.778975629913174, -1.04402814519501], [0.8442... \n",
- "39 [[-0.645092069902244, 0.04920122139904896, 0.21081031345366397], [1.14893686034324, -0.6114894792387109, -0.40665475671972495], [-0.287092609914331, 0.292443458702414, -0.801010316546939], [-0.184... \n",
+ "30 [[-0.570999988178138, 1.09833300989088, -0.566585257198733], [-0.0492000252908119, 0.59741650677863, 0.966635869516026], [-0.0024866801464869, -0.0674126479000064, 0.2546322145358], [0.22890858859... \n",
+ "31 [[-0.555658749547297, 0.42566314828364293, 0.466167261187242], [-1.39449792316991, -0.5017985009363141, 0.44565311708876987], [1.25410857633047, -0.33111996310053693, -0.24658722383835793], [0.391... \n",
+ "32 [[0.550884754551255, 0.33865983742361505, 0.12042096866994706], [0.189727589275785, -0.524367356684469, -0.17886090450817402], [-1.6758650723095, -0.3103026148332811, 0.6946509585210929], [0.44535... \n",
+ "33 [[0.235449441283078, 0.601163461328045, 0.047374581092271446], [0.337344266120139, 0.664857294034072, 0.37768738132641905], [-0.171831038532397, -0.11286098092806901, -0.07415135874388401], [-0.19... \n",
+ "34 [[1.15453322332514, 0.04603660924936107, -1.21282260886917], [-0.40339091367313, -0.18969536874822704, 0.030526533197075965], [-0.683024325118812, -0.967333273080052, 0.4114320467711059], [-0.8675... \n",
+ "35 [[0.742004122720721, 0.22577315892753805, 0.12298868178080906], [0.771069504023197, -0.90014541932296, -0.660403448735684], [-0.462456173471762, 1.24796716627208, -0.41920103505081097], [0.0821911... \n",
+ "36 [[0.552749731039339, -0.46087407706439093, 0.177277816520108], [0.340079509796374, 0.23574679643604202, 0.26784307872275903], [0.38829857840138, 0.331969932800717, 0.34399524631836903], [-0.598269... \n",
+ "37 [[0.570532228170616, 0.17193739346638104, 0.796311675023456], [0.672970110578221, -0.817732243959863, 0.207420342553793], [0.391385096556072, -0.862073619432422, 0.597813478903035], [-0.2704527267... \n",
+ "38 [[-1.11093024775332, -0.6986058375434832, 0.4467412161766239], [0.0416051560885089, -0.0424634968511256, -0.0874515614131588], [-0.135122617107614, -0.778975629931571, -1.04402814517475], [0.84425... \n",
+ "39 [[-0.645092069842764, 0.049201221165188956, 0.21081031392484997], [1.14893686212593, -0.6114894802487358, -0.40665475714370897], [-0.287092610235497, 0.292443460035198, -0.801010315713759], [-0.18... \n",
"\n",
" number_of_atoms \n",
"0 1.0 \n",
@@ -1677,7 +1150,7 @@
"39 128.0 "
]
},
- "execution_count": 35,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -1689,7 +1162,7 @@
},
{
"cell_type": "markdown",
- "id": "level-dimension",
+ "id": "crazy-clerk",
"metadata": {},
"source": [
"We can now inspect the data in this dataset quite easily"
@@ -1697,8 +1170,8 @@
},
{
"cell_type": "code",
- "execution_count": 36,
- "id": "visible-execution",
+ "execution_count": 17,
+ "id": "third-comfort",
"metadata": {},
"outputs": [],
"source": [
@@ -1707,8 +1180,8 @@
},
{
"cell_type": "code",
- "execution_count": 37,
- "id": "becoming-integral",
+ "execution_count": 18,
+ "id": "magnetic-spring",
"metadata": {},
"outputs": [],
"source": [
@@ -1717,7 +1190,7 @@
},
{
"cell_type": "markdown",
- "id": "novel-usage",
+ "id": "expired-savage",
"metadata": {},
"source": [
"The datasets used in the potential fitting procedure for day 2 (obtained from accurate DFT calculations) will be accessed in the same way"
@@ -1726,7 +1199,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "forced-scotland",
+ "id": "regular-branch",
"metadata": {},
"outputs": [],
"source": []
@@ -1748,7 +1221,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.8"
+ "version": "3.8.6"
}
},
"nbformat": 4,
diff --git a/day_2/00-IntroductionDay2.ipynb b/day_2/00-IntroductionDay2.ipynb
index fb3cd58d78215ebfede75cd09ca91caf2fa8e9f3..37b0bfdb12f19435fe97ca76b148729aa310ae7f 100644
--- a/day_2/00-IntroductionDay2.ipynb
+++ b/day_2/00-IntroductionDay2.ipynb
@@ -2,6 +2,7 @@
"cells": [
{
"cell_type": "markdown",
+ "id": "infectious-lingerie",
"metadata": {},
"source": [
"# Day 2 - Parameterization of interatomic potentials"
@@ -9,6 +10,7 @@
},
{
"cell_type": "markdown",
+ "id": "destroyed-simulation",
"metadata": {},
"source": [
"In this tutorial we will do simple fits for three different interatomic potentials.\n",
@@ -21,6 +23,7 @@
},
{
"cell_type": "markdown",
+ "id": "prescribed-campbell",
"metadata": {},
"source": [
"## Embedded Atom Method Potential"
@@ -28,6 +31,7 @@
},
{
"cell_type": "markdown",
+ "id": "dress-gauge",
"metadata": {},
"source": [
"* Atomic descriptors: pair functions\n",
@@ -45,6 +49,7 @@
},
{
"cell_type": "markdown",
+ "id": "tribal-intro",
"metadata": {},
"source": [
"## Neural Network Potential"
@@ -52,6 +57,7 @@
},
{
"cell_type": "markdown",
+ "id": "imported-answer",
"metadata": {},
"source": [
"* Atomic descriptors: pair and three-body symmetry functions\n",
@@ -69,6 +75,7 @@
},
{
"cell_type": "markdown",
+ "id": "obvious-finish",
"metadata": {},
"source": [
"## Atomic Cluster Expansion"
@@ -76,6 +83,7 @@
},
{
"cell_type": "markdown",
+ "id": "successful-maine",
"metadata": {},
"source": [
"* Atomic descriptors: pair, three-body, ... many-body basis functions\n",
@@ -93,6 +101,7 @@
},
{
"cell_type": "markdown",
+ "id": "deluxe-recording",
"metadata": {},
"source": [
"# Reference data"
@@ -100,6 +109,7 @@
},
{
"cell_type": "markdown",
+ "id": "accessible-criminal",
"metadata": {},
"source": [
"The potentials are parameterized by fitting to reference data. Here we use DFT data for Cu that we generated with the FHI-aims code. In the following we summarize key properties of the dataset."
@@ -108,6 +118,7 @@
{
"cell_type": "code",
"execution_count": 1,
+ "id": "literary-discovery",
"metadata": {},
"outputs": [
{
@@ -125,6 +136,7 @@
{
"cell_type": "code",
"execution_count": 2,
+ "id": "sunrise-siemens",
"metadata": {},
"outputs": [],
"source": [
@@ -134,6 +146,7 @@
{
"cell_type": "code",
"execution_count": 3,
+ "id": "educational-toddler",
"metadata": {},
"outputs": [],
"source": [
@@ -143,6 +156,7 @@
{
"cell_type": "code",
"execution_count": 4,
+ "id": "recorded-guitar",
"metadata": {},
"outputs": [],
"source": [
@@ -155,6 +169,7 @@
{
"cell_type": "code",
"execution_count": 5,
+ "id": "infinite-genesis",
"metadata": {},
"outputs": [],
"source": [
@@ -164,6 +179,7 @@
{
"cell_type": "code",
"execution_count": 6,
+ "id": "social-recycling",
"metadata": {},
"outputs": [
{
@@ -207,87 +223,274 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>286</td>\n",
+ " <td>1</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df1_A1_A2_A3_EV_elast_phon</td>\n",
" <td>/df1_A1_A2_A3_EV_elast_phon</td>\n",
- " <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
- " <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
- " <td>2021-02-08 10:33:52.341472</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:53.061360</td>\n",
+ " <td>NaT</td>\n",
+ " <td>NaN</td>\n",
" <td>zora@cmti001#1</td>\n",
" <td>TrainingContainer</td>\n",
" <td>0.4</td>\n",
" <td>None</td>\n",
- " <td>None</td>\n",
+ " <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>287</td>\n",
+ " <td>2</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df3_10k</td>\n",
" <td>/df3_10k</td>\n",
- " <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
- " <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
- " <td>2021-02-08 10:33:53.993230</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:55.496691</td>\n",
+ " <td>NaT</td>\n",
+ " <td>NaN</td>\n",
" <td>zora@cmti001#1</td>\n",
" <td>TrainingContainer</td>\n",
" <td>0.4</td>\n",
" <td>None</td>\n",
- " <td>None</td>\n",
+ " <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>288</td>\n",
+ " <td>3</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df2_1k</td>\n",
" <td>/df2_1k</td>\n",
- " <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
- " <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
- " <td>2021-02-08 10:33:54.435308</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:56.101883</td>\n",
+ " <td>NaT</td>\n",
+ " <td>NaN</td>\n",
+ " <td>zora@cmti001#1</td>\n",
+ " <td>TrainingContainer</td>\n",
+ " <td>0.4</td>\n",
" <td>None</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>finished</td>\n",
" <td>None</td>\n",
+ " <td>df4_2_5eV_25A3_8K</td>\n",
+ " <td>/df4_2_5eV_25A3_8K</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:57.547918</td>\n",
+ " <td>NaT</td>\n",
+ " <td>NaN</td>\n",
" <td>zora@cmti001#1</td>\n",
" <td>TrainingContainer</td>\n",
" <td>0.4</td>\n",
" <td>None</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu108</td>\n",
+ " <td>lammps_job</td>\n",
+ " <td>/lammps_job</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/first_steps/</td>\n",
+ " <td>2021-03-09 08:58:10.515085</td>\n",
+ " <td>2021-03-09 08:58:14.811278</td>\n",
+ " <td>4.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>302</th>\n",
+ " <td>303</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>lammps_struct_7</td>\n",
+ " <td>/lammps_struct_7</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_3/validation/Cu-atomicrex-df1-107-25/</td>\n",
+ " <td>2021-03-09 09:40:07.733451</td>\n",
+ " <td>2021-03-09 09:40:08.166608</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>303</th>\n",
+ " <td>304</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>lammps_struct_8</td>\n",
+ " <td>/lammps_struct_8</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_3/validation/Cu-atomicrex-df1-107-25/</td>\n",
+ " <td>2021-03-09 09:40:25.020783</td>\n",
+ " <td>2021-03-09 09:40:25.465015</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>304</th>\n",
+ " <td>305</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>lammps_struct_9</td>\n",
+ " <td>/lammps_struct_9</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_3/validation/Cu-atomicrex-df1-107-25/</td>\n",
+ " <td>2021-03-09 09:40:42.552932</td>\n",
+ " <td>2021-03-09 09:40:42.978853</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
" <td>None</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>305</th>\n",
+ " <td>306</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>lammps_struct_10</td>\n",
+ " <td>/lammps_struct_10</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_3/validation/Cu-atomicrex-df1-107-25/</td>\n",
+ " <td>2021-03-09 09:41:00.324055</td>\n",
+ " <td>2021-03-09 09:41:00.761452</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>306</th>\n",
+ " <td>307</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>lammps_box</td>\n",
+ " <td>/lammps_box</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_3/validation/Cu-atomicrex-df1-107-25/</td>\n",
+ " <td>2021-03-09 09:41:17.387798</td>\n",
+ " <td>2021-03-09 09:41:17.863638</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
+ "<p>307 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
- " id status chemicalformula job \\\n",
- "0 286 finished None df1_A1_A2_A3_EV_elast_phon \n",
- "1 287 finished None df3_10k \n",
- "2 288 finished None df2_1k \n",
+ " id status chemicalformula job \\\n",
+ "0 1 finished None df1_A1_A2_A3_EV_elast_phon \n",
+ "1 2 finished None df3_10k \n",
+ "2 3 finished None df2_1k \n",
+ "3 4 finished None df4_2_5eV_25A3_8K \n",
+ "4 5 finished Cu108 lammps_job \n",
+ ".. ... ... ... ... \n",
+ "302 303 finished Cu lammps_struct_7 \n",
+ "303 304 finished Cu lammps_struct_8 \n",
+ "304 305 finished Cu lammps_struct_9 \n",
+ "305 306 finished Cu lammps_struct_10 \n",
+ "306 307 finished Cu lammps_box \n",
+ "\n",
+ " subjob projectpath \\\n",
+ "0 /df1_A1_A2_A3_EV_elast_phon /home/pyiron/ \n",
+ "1 /df3_10k /home/pyiron/ \n",
+ "2 /df2_1k /home/pyiron/ \n",
+ "3 /df4_2_5eV_25A3_8K /home/pyiron/ \n",
+ "4 /lammps_job /home/pyiron/ \n",
+ ".. ... ... \n",
+ "302 /lammps_struct_7 /home/pyiron/ \n",
+ "303 /lammps_struct_8 /home/pyiron/ \n",
+ "304 /lammps_struct_9 /home/pyiron/ \n",
+ "305 /lammps_struct_10 /home/pyiron/ \n",
+ "306 /lammps_box /home/pyiron/ \n",
"\n",
- " subjob projectpath \\\n",
- "0 /df1_A1_A2_A3_EV_elast_phon /home/yury/PycharmProjects/pyiron-2021/ \n",
- "1 /df3_10k /home/yury/PycharmProjects/pyiron-2021/ \n",
- "2 /df2_1k /home/yury/PycharmProjects/pyiron-2021/ \n",
+ " project timestart \\\n",
+ "0 datasets/Cu_database/ 2021-02-18 19:49:53.061360 \n",
+ "1 datasets/Cu_database/ 2021-02-18 19:49:55.496691 \n",
+ "2 datasets/Cu_database/ 2021-02-18 19:49:56.101883 \n",
+ "3 datasets/Cu_database/ 2021-02-18 19:49:57.547918 \n",
+ "4 day_1/first_steps/ 2021-03-09 08:58:10.515085 \n",
+ ".. ... ... \n",
+ "302 day_3/validation/Cu-atomicrex-df1-107-25/ 2021-03-09 09:40:07.733451 \n",
+ "303 day_3/validation/Cu-atomicrex-df1-107-25/ 2021-03-09 09:40:25.020783 \n",
+ "304 day_3/validation/Cu-atomicrex-df1-107-25/ 2021-03-09 09:40:42.552932 \n",
+ "305 day_3/validation/Cu-atomicrex-df1-107-25/ 2021-03-09 09:41:00.324055 \n",
+ "306 day_3/validation/Cu-atomicrex-df1-107-25/ 2021-03-09 09:41:17.387798 \n",
"\n",
- " project \\\n",
- "0 pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
- "1 pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
- "2 pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
+ " timestop totalcputime computer \\\n",
+ "0 NaT NaN zora@cmti001#1 \n",
+ "1 NaT NaN zora@cmti001#1 \n",
+ "2 NaT NaN zora@cmti001#1 \n",
+ "3 NaT NaN zora@cmti001#1 \n",
+ "4 2021-03-09 08:58:14.811278 4.0 pyiron@jupyter-janssen#1 \n",
+ ".. ... ... ... \n",
+ "302 2021-03-09 09:40:08.166608 0.0 pyiron@jupyter-janssen#1 \n",
+ "303 2021-03-09 09:40:25.465015 0.0 pyiron@jupyter-janssen#1 \n",
+ "304 2021-03-09 09:40:42.978853 0.0 pyiron@jupyter-janssen#1 \n",
+ "305 2021-03-09 09:41:00.761452 0.0 pyiron@jupyter-janssen#1 \n",
+ "306 2021-03-09 09:41:17.863638 0.0 pyiron@jupyter-janssen#1 \n",
"\n",
- " timestart timestop totalcputime computer \\\n",
- "0 2021-02-08 10:33:52.341472 None None zora@cmti001#1 \n",
- "1 2021-02-08 10:33:53.993230 None None zora@cmti001#1 \n",
- "2 2021-02-08 10:33:54.435308 None None zora@cmti001#1 \n",
+ " hamilton hamversion parentid masterid \n",
+ "0 TrainingContainer 0.4 None NaN \n",
+ "1 TrainingContainer 0.4 None NaN \n",
+ "2 TrainingContainer 0.4 None NaN \n",
+ "3 TrainingContainer 0.4 None NaN \n",
+ "4 Lammps 0.1 None NaN \n",
+ ".. ... ... ... ... \n",
+ "302 Lammps 0.1 None NaN \n",
+ "303 Lammps 0.1 None NaN \n",
+ "304 Lammps 0.1 None NaN \n",
+ "305 Lammps 0.1 None NaN \n",
+ "306 Lammps 0.1 None NaN \n",
"\n",
- " hamilton hamversion parentid masterid \n",
- "0 TrainingContainer 0.4 None None \n",
- "1 TrainingContainer 0.4 None None \n",
- "2 TrainingContainer 0.4 None None "
+ "[307 rows x 15 columns]"
]
},
"execution_count": 6,
@@ -302,6 +505,7 @@
{
"cell_type": "code",
"execution_count": 7,
+ "id": "documented-liberia",
"metadata": {},
"outputs": [],
"source": [
@@ -313,6 +517,7 @@
{
"cell_type": "code",
"execution_count": 8,
+ "id": "tropical-revision",
"metadata": {},
"outputs": [],
"source": [
@@ -326,6 +531,7 @@
{
"cell_type": "code",
"execution_count": 9,
+ "id": "everyday-anthropology",
"metadata": {},
"outputs": [],
"source": [
@@ -336,6 +542,7 @@
{
"cell_type": "code",
"execution_count": 10,
+ "id": "suffering-following",
"metadata": {},
"outputs": [],
"source": [
@@ -347,6 +554,7 @@
{
"cell_type": "code",
"execution_count": 11,
+ "id": "featured-coalition",
"metadata": {},
"outputs": [
{
@@ -417,6 +625,7 @@
},
{
"cell_type": "markdown",
+ "id": "viral-bedroom",
"metadata": {},
"source": [
"* Dataset1 (df1): 105 structures: E-V, elastic matrix and phonopy deformations for fcc, bcc, hcp\n",
@@ -427,6 +636,7 @@
{
"cell_type": "code",
"execution_count": 12,
+ "id": "greater-louisiana",
"metadata": {},
"outputs": [
{
@@ -466,12 +676,13 @@
{
"cell_type": "code",
"execution_count": 13,
+ "id": "threaded-tomato",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.legend.Legend at 0x7f9588176fd0>"
+ "<matplotlib.legend.Legend at 0x7f3b359c2940>"
]
},
"execution_count": 13,
@@ -508,6 +719,7 @@
{
"cell_type": "code",
"execution_count": null,
+ "id": "hybrid-allowance",
"metadata": {},
"outputs": [],
"source": []
@@ -529,7 +741,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.6"
+ "version": "3.8.6"
}
},
"nbformat": 4,
diff --git a/day_2/01-atomicrex/WorkshopPotentialEAM.ipynb b/day_2/01-atomicrex/WorkshopPotentialEAM.ipynb
index aca7a959230c25a504d0dd8a7b610fdcefe4af85..83301a2d34c6c551a64f09a3a738b20c3276db94 100644
--- a/day_2/01-atomicrex/WorkshopPotentialEAM.ipynb
+++ b/day_2/01-atomicrex/WorkshopPotentialEAM.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "entitled-break",
+ "id": "independent-canyon",
"metadata": {},
"source": [
"# Fitting an EAM potential\n",
@@ -27,7 +27,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "boring-simon",
+ "id": "usual-tomato",
"metadata": {},
"outputs": [],
"source": [
@@ -40,7 +40,7 @@
},
{
"cell_type": "markdown",
- "id": "southwest-southwest",
+ "id": "australian-lottery",
"metadata": {},
"source": [
"### Import the training data"
@@ -49,7 +49,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "adaptive-yacht",
+ "id": "liable-penny",
"metadata": {},
"outputs": [
{
@@ -93,14 +93,14 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>759</td>\n",
+ " <td>1</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df1_A1_A2_A3_EV_elast_phon</td>\n",
" <td>/df1_A1_A2_A3_EV_elast_phon</td>\n",
- " <td>/home/niklas/pyiron/projects/</td>\n",
- " <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
- " <td>2021-02-08 10:33:52.341472</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:53.061360</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>zora@cmti001#1</td>\n",
@@ -111,14 +111,14 @@
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>760</td>\n",
+ " <td>2</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df3_10k</td>\n",
" <td>/df3_10k</td>\n",
- " <td>/home/niklas/pyiron/projects/</td>\n",
- " <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
- " <td>2021-02-08 10:33:53.993230</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:55.496691</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>zora@cmti001#1</td>\n",
@@ -129,14 +129,32 @@
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>761</td>\n",
+ " <td>3</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df2_1k</td>\n",
" <td>/df2_1k</td>\n",
- " <td>/home/niklas/pyiron/projects/</td>\n",
- " <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
- " <td>2021-02-08 10:33:54.435308</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:56.101883</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>zora@cmti001#1</td>\n",
+ " <td>TrainingContainer</td>\n",
+ " <td>0.4</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>finished</td>\n",
+ " <td>None</td>\n",
+ " <td>df4_2_5eV_25A3_8K</td>\n",
+ " <td>/df4_2_5eV_25A3_8K</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:57.547918</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>zora@cmti001#1</td>\n",
@@ -150,30 +168,29 @@
"</div>"
],
"text/plain": [
- " id status chemicalformula job \\\n",
- "0 759 finished None df1_A1_A2_A3_EV_elast_phon \n",
- "1 760 finished None df3_10k \n",
- "2 761 finished None df2_1k \n",
- "\n",
- " subjob projectpath \\\n",
- "0 /df1_A1_A2_A3_EV_elast_phon /home/niklas/pyiron/projects/ \n",
- "1 /df3_10k /home/niklas/pyiron/projects/ \n",
- "2 /df2_1k /home/niklas/pyiron/projects/ \n",
+ " id status chemicalformula job \\\n",
+ "0 1 finished None df1_A1_A2_A3_EV_elast_phon \n",
+ "1 2 finished None df3_10k \n",
+ "2 3 finished None df2_1k \n",
+ "3 4 finished None df4_2_5eV_25A3_8K \n",
"\n",
- " project \\\n",
- "0 pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
- "1 pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
- "2 pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
+ " subjob projectpath project \\\n",
+ "0 /df1_A1_A2_A3_EV_elast_phon /home/pyiron/ datasets/Cu_database/ \n",
+ "1 /df3_10k /home/pyiron/ datasets/Cu_database/ \n",
+ "2 /df2_1k /home/pyiron/ datasets/Cu_database/ \n",
+ "3 /df4_2_5eV_25A3_8K /home/pyiron/ datasets/Cu_database/ \n",
"\n",
" timestart timestop totalcputime computer \\\n",
- "0 2021-02-08 10:33:52.341472 None None zora@cmti001#1 \n",
- "1 2021-02-08 10:33:53.993230 None None zora@cmti001#1 \n",
- "2 2021-02-08 10:33:54.435308 None None zora@cmti001#1 \n",
+ "0 2021-02-18 19:49:53.061360 None None zora@cmti001#1 \n",
+ "1 2021-02-18 19:49:55.496691 None None zora@cmti001#1 \n",
+ "2 2021-02-18 19:49:56.101883 None None zora@cmti001#1 \n",
+ "3 2021-02-18 19:49:57.547918 None None zora@cmti001#1 \n",
"\n",
" hamilton hamversion parentid masterid \n",
"0 TrainingContainer 0.4 None None \n",
"1 TrainingContainer 0.4 None None \n",
- "2 TrainingContainer 0.4 None None "
+ "2 TrainingContainer 0.4 None None \n",
+ "3 TrainingContainer 0.4 None None "
]
},
"execution_count": 2,
@@ -191,7 +208,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "whole-dragon",
+ "id": "emerging-writing",
"metadata": {},
"outputs": [],
"source": [
@@ -202,17 +219,9 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "portuguese-trance",
+ "id": "federal-biology",
"metadata": {},
- "outputs": [
- {
- "name": "stdin",
- "output_type": "stream",
- "text": [
- "Are you sure you want to delete all jobs from 'WorkshopPotential'? y/(n) y\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pr = Project(\"WorkshopPotential\")\n",
"#pr.remove_jobs()"
@@ -220,7 +229,7 @@
},
{
"cell_type": "markdown",
- "id": "vanilla-asset",
+ "id": "palestinian-microwave",
"metadata": {},
"source": [
"### Create an atomicrex job"
@@ -229,7 +238,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "characteristic-corruption",
+ "id": "overhead-complement",
"metadata": {},
"outputs": [],
"source": [
@@ -238,7 +247,7 @@
},
{
"cell_type": "markdown",
- "id": "competent-produce",
+ "id": "behind-conditioning",
"metadata": {},
"source": [
"### Add the structures that should be fitted.\n",
@@ -248,7 +257,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "streaming-distance",
+ "id": "fifteen-prototype",
"metadata": {},
"outputs": [],
"source": [
@@ -261,7 +270,7 @@
},
{
"cell_type": "markdown",
- "id": "acoustic-developer",
+ "id": "alpine-routine",
"metadata": {},
"source": [
"### Define the type of potential and necessary functions.\n",
@@ -271,7 +280,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "compliant-pharmacy",
+ "id": "future-capture",
"metadata": {},
"outputs": [],
"source": [
@@ -280,7 +289,7 @@
},
{
"cell_type": "markdown",
- "id": "respective-writer",
+ "id": "floating-equilibrium",
"metadata": {},
"source": [
"It is necessary to define a pair potential, an electronic density function and an embedding function.\n",
@@ -301,7 +310,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "electric-corps",
+ "id": "exact-course",
"metadata": {},
"outputs": [],
"source": [
@@ -310,7 +319,7 @@
},
{
"cell_type": "markdown",
- "id": "retired-motor",
+ "id": "seeing-compilation",
"metadata": {},
"source": [
"Pre defined functions like the morse function can be plotted to see the influence of the initial parameter values"
@@ -319,7 +328,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "id": "affected-tamil",
+ "id": "senior-graphic",
"metadata": {},
"outputs": [
{
@@ -352,7 +361,7 @@
},
{
"cell_type": "markdown",
- "id": "forced-listing",
+ "id": "interim-empty",
"metadata": {},
"source": [
"Additionally it is a good idea to define limits for the parameters. This is optional for local minimizers, but the fit can quickly run away without limits. Global optimizers typically require them to constrain the sampled space."
@@ -361,7 +370,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "id": "sunset-beginning",
+ "id": "traditional-transmission",
"metadata": {},
"outputs": [],
"source": [
@@ -379,7 +388,7 @@
},
{
"cell_type": "markdown",
- "id": "raised-kansas",
+ "id": "bibliographic-brick",
"metadata": {},
"source": [
"A screening function needs to be defined for the morse potential"
@@ -388,7 +397,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "republican-rabbit",
+ "id": "recreational-intelligence",
"metadata": {},
"outputs": [],
"source": [
@@ -397,7 +406,7 @@
},
{
"cell_type": "markdown",
- "id": "diverse-smith",
+ "id": "voluntary-dressing",
"metadata": {},
"source": [
"The electron density is chosen to be a spline function. The cutoff has to be defined. Derivatives left and right are optional, they default to 0. For the right cutoff this is fine, since the forces should smoothly go to 0. For the left this is not necessarily the best choice, since the function value should increase at very close distances. Very large absolute values will lead to osciallations and should be avoided."
@@ -406,7 +415,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "id": "described-blond",
+ "id": "aging-frontier",
"metadata": {},
"outputs": [],
"source": [
@@ -415,7 +424,7 @@
},
{
"cell_type": "markdown",
- "id": "solved-debut",
+ "id": "relevant-commissioner",
"metadata": {},
"source": [
"For a spline function it is necessary to define node points. They can be equally spaced or sampled with higher density around turning points, f.e. the first neighbor distance.\n",
@@ -425,7 +434,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "hazardous-lying",
+ "id": "adaptive-chicago",
"metadata": {},
"outputs": [
{
@@ -446,7 +455,7 @@
},
{
"cell_type": "markdown",
- "id": "blocked-volleyball",
+ "id": "analyzed-dream",
"metadata": {},
"source": [
"The nodes need initial values. The electron density should be proportional to $e^{-r}$, so this function is chosen to calculate them."
@@ -455,7 +464,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "id": "geographic-legislation",
+ "id": "reserved-cuisine",
"metadata": {},
"outputs": [],
"source": [
@@ -465,7 +474,7 @@
},
{
"cell_type": "markdown",
- "id": "outdoor-seven",
+ "id": "august-johnston",
"metadata": {},
"source": [
"A density can't be negative so the lower limit is set to 0. The upper limit is chosen to be 3 times the initial values. These choices aswell as the choice for $e^{-r}$ as initial values are somewhat arbitrary. The electron density from single atoms does not directly influence the calculated energies and forces, instead the summed up density at some place is used in the embedding function, so the final numerical values are an interplay between electron density and embedding function. Since the latter will also be a spline function it can only be defined for a certain range of rho values as node points. Therefore it is better to limit the range of electron density values and define larger limits for the embedding function instead. "
@@ -474,7 +483,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "collectible-viewer",
+ "id": "color-condition",
"metadata": {},
"outputs": [],
"source": [
@@ -485,7 +494,7 @@
},
{
"cell_type": "raw",
- "id": "bottom-potato",
+ "id": "excellent-pencil",
"metadata": {},
"source": [
"Finally the last node point at the cutoff range is set to 0 and fitting is disabled to prevent a discontinuous change of energy at the cutoff."
@@ -494,7 +503,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "id": "narrow-hearts",
+ "id": "strong-beijing",
"metadata": {},
"outputs": [],
"source": [
@@ -504,7 +513,7 @@
},
{
"cell_type": "markdown",
- "id": "miniature-department",
+ "id": "liable-penguin",
"metadata": {},
"source": [
"$-\\sqrt(\\rho)$ can be used as initial guess for the embedding energy, which is taken from second moment approximation tight binding. \n",
@@ -516,7 +525,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "id": "younger-freight",
+ "id": "comfortable-roads",
"metadata": {},
"outputs": [],
"source": [
@@ -534,7 +543,7 @@
},
{
"cell_type": "markdown",
- "id": "alert-stake",
+ "id": "ideal-toolbox",
"metadata": {},
"source": [
"The functions have to be assigned to the potential"
@@ -543,7 +552,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "banned-allocation",
+ "id": "filled-reminder",
"metadata": {},
"outputs": [],
"source": [
@@ -554,7 +563,7 @@
},
{
"cell_type": "markdown",
- "id": "positive-frost",
+ "id": "connected-principle",
"metadata": {},
"source": [
"### Define fitting procedure\n",
@@ -565,26 +574,162 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "existing-average",
+ "id": "blond-drawing",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job PotentialDF1 was saved and received the ID: 848\n"
+ "The job PotentialDF1 was saved and received the ID: 55\n"
]
},
{
"data": {
"application/json": {
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3.96060e-01, 3.93015e-01, 3.90651e-01,\n 3.90651e-01, 3.83317e-01, 3.83317e-01, 3.75523e-01, 3.75523e-01,\n 3.74460e-01, 3.74460e-01, 3.73550e-01, 3.73550e-01, 3.71117e-01,\n 3.71117e-01, 3.67281e-01, 3.67281e-01, 3.60663e-01, 3.60663e-01,\n 3.54458e-01, 3.54458e-01, 3.49244e-01, 3.49244e-01, 3.46727e-01,\n 3.46727e-01, 3.45318e-01, 3.45318e-01, 3.44284e-01, 3.44284e-01,\n 3.41980e-01, 3.41980e-01, 3.35753e-01, 3.35753e-01, 3.35753e-01,\n 3.35489e-01, 3.35489e-01, 3.29343e-01, 3.29343e-01, 3.29343e-01,\n 3.26769e-01, 3.26769e-01, 3.19913e-01, 3.19913e-01, 3.18384e-01,\n 3.18384e-01, 3.18198e-01, 3.18198e-01, 3.18103e-01, 3.18103e-01,\n 3.17873e-01, 3.17873e-01, 3.17741e-01, 3.17741e-01, 3.16377e-01,\n 3.16377e-01, 3.13142e-01, 3.13142e-01, 3.12609e-01, 3.12609e-01,\n 3.12044e-01, 3.12044e-01, 3.11984e-01, 3.11984e-01, 3.06905e-01,\n 3.06905e-01, 2.84829e-01, 2.84829e-01, 2.84787e-01, 2.84787e-01,\n 2.84763e-01, 2.84763e-01, 2.84734e-01, 2.84734e-01, 2.82886e-01,\n 2.82886e-01, 2.79732e-01, 2.79732e-01, 2.78938e-01, 2.78938e-01,\n 2.78624e-01, 2.78624e-01, 2.78593e-01, 2.78593e-01, 2.78544e-01,\n 2.78544e-01, 2.78507e-01, 2.78507e-01, 2.78341e-01, 2.78341e-01,\n 2.78329e-01, 2.78329e-01, 2.78170e-01, 2.78170e-01, 2.78030e-01,\n 2.78030e-01, 2.77994e-01, 2.77994e-01, 2.77945e-01, 2.77945e-01,\n 2.77905e-01, 2.77905e-01, 2.77905e-01, 2.77905e-01, 2.77893e-01,\n 2.77893e-01, 2.77826e-01, 2.77826e-01, 2.77565e-01, 2.77565e-01,\n 2.77247e-01, 2.77247e-01, 2.76692e-01, 2.76692e-01, 2.76165e-01,\n 2.76165e-01, 2.74387e-01, 2.74387e-01, 2.71900e-01, 2.71900e-01,\n 2.65026e-01, 2.65026e-01, 2.62625e-01, 2.54725e-01, 2.54725e-01,\n 2.54725e-01, 2.36225e-01, 2.36225e-01, 2.21180e-01, 2.21180e-01,\n 2.06805e-01, 2.06805e-01, 1.99498e-01, 1.99498e-01, 1.98107e-01,\n 1.98107e-01, 1.96974e-01, 1.96974e-01, 1.95735e-01, 1.95735e-01,\n 1.94396e-01, 1.94396e-01, 1.92781e-01, 1.92781e-01, 1.91211e-01,\n 1.91211e-01, 1.89741e-01, 1.89741e-01, 1.85730e-01, 1.85730e-01,\n 1.85730e-01, 1.83799e-01, 1.83799e-01, 1.80274e-01, 1.80274e-01,\n 1.80045e-01, 1.80045e-01, 1.79917e-01, 1.79917e-01, 1.78872e-01,\n 1.78872e-01, 1.76442e-01, 1.76442e-01, 1.75316e-01, 1.75316e-01,\n 1.74677e-01, 1.74677e-01, 1.74293e-01, 1.74293e-01, 1.73979e-01,\n 1.73979e-01, 1.73979e-01, 1.73850e-01, 1.73850e-01, 1.73720e-01,\n 1.73720e-01, 1.73691e-01, 1.73691e-01, 1.73677e-01, 1.73677e-01,\n 1.73661e-01, 1.73661e-01, 1.73647e-01, 1.73647e-01, 1.73642e-01,\n 1.73642e-01, 1.73637e-01, 1.73637e-01, 1.73631e-01, 1.73631e-01,\n 1.73610e-01, 1.73610e-01, 1.73557e-01, 1.73557e-01, 1.73432e-01,\n 1.73432e-01, 1.73432e-01, 1.73383e-01, 1.73383e-01, 1.73129e-01,\n 1.73129e-01, 1.72543e-01, 1.72543e-01, 1.71775e-01, 1.71775e-01,\n 1.71372e-01, 1.71372e-01, 1.70823e-01, 1.70823e-01, 1.70537e-01,\n 1.70537e-01, 1.70537e-01, 1.70463e-01, 1.70463e-01, 1.70093e-01,\n 1.70093e-01, 1.69113e-01, 1.69113e-01, 1.68421e-01, 1.68421e-01,\n 1.68257e-01, 1.68257e-01, 1.68236e-01, 1.68236e-01, 1.68227e-01,\n 1.68227e-01, 1.68220e-01, 1.68220e-01, 1.68211e-01, 1.68211e-01,\n 1.68205e-01, 1.68205e-01, 1.68199e-01, 1.68199e-01, 1.68186e-01,\n 1.68186e-01, 1.68170e-01, 1.68170e-01, 1.68153e-01, 1.68153e-01,\n 1.68076e-01, 1.68076e-01, 1.67983e-01, 1.67983e-01, 1.67854e-01,\n 1.67854e-01, 1.67691e-01, 1.67691e-01, 1.67691e-01, 1.67674e-01,\n 1.67674e-01, 1.67648e-01, 1.67648e-01, 1.66781e-01, 1.66781e-01,\n 1.65890e-01, 1.65890e-01, 1.65890e-01, 1.65363e-01, 1.65363e-01,\n 1.65164e-01, 1.65164e-01, 1.64561e-01, 1.64561e-01, 1.64442e-01,\n 1.64442e-01, 1.64333e-01, 1.64333e-01, 1.64304e-01, 1.64304e-01,\n 1.64297e-01, 1.64297e-01, 1.64288e-01, 1.64288e-01, 1.64279e-01,\n 1.64279e-01, 1.64276e-01, 1.64276e-01, 1.64274e-01, 1.64274e-01,\n 1.64274e-01, 1.64274e-01, 1.64273e-01, 1.64273e-01, 1.64273e-01,\n 1.64273e-01, 1.64273e-01, 1.64273e-01, 1.64273e-01, 1.64273e-01,\n 1.64273e-01, 1.64273e-01, 1.64273e-01, 1.64273e-01, 1.64273e-01,\n 1.64272e-01, 1.64272e-01, 1.64272e-01, 1.64272e-01, 1.64270e-01,\n 1.64270e-01, 1.64268e-01, 1.64268e-01, 1.64265e-01, 1.64265e-01,\n 1.64263e-01, 1.64263e-01, 1.64263e-01, 1.64262e-01, 1.64262e-01,\n 1.64262e-01, 1.64262e-01, 1.64262e-01, 1.64262e-01, 1.64262e-01,\n 1.64261e-01, 1.64261e-01, 1.64261e-01, 1.64261e-01, 1.64261e-01,\n 1.64261e-01, 1.64260e-01, 1.64260e-01, 1.64260e-01, 1.64260e-01,\n 1.64260e-01, 1.64259e-01, 1.64259e-01, 1.64259e-01, 1.64255e-01,\n 1.64255e-01, 1.64244e-01, 1.64244e-01, 1.64244e-01, 1.64243e-01,\n 1.64243e-01, 1.64235e-01, 1.64235e-01, 1.64210e-01, 1.64210e-01,\n 1.64210e-01, 1.64209e-01, 1.64209e-01, 1.64201e-01, 1.64201e-01,\n 1.64177e-01, 1.64177e-01, 1.64138e-01, 1.64138e-01, 1.64138e-01,\n 1.64138e-01, 1.64138e-01, 1.64027e-01, 1.64027e-01, 1.64005e-01])"
},
"text/plain": [
- "Output({'error': None, 'residual': array([1.39371e+03, 1.39371e+03, 1.39371e+03, ..., 1.52231e-01,\n",
- " 1.52231e-01, 1.52231e-01]), 'iterations': array([ 1, 2, 3, ..., 1998, 1999, 2000], dtype=uint32)})"
+ "Output({'error': None, 'residual': array([1.39371e+03, 1.39371e+03, 1.39371e+03, 1.39285e+02, 1.39285e+02,\n",
+ " 1.37569e+02, 1.32438e+02, 1.32438e+02, 1.27976e+02, 1.27976e+02,\n",
+ " 1.25789e+02, 1.25789e+02, 1.11972e+02, 1.11972e+02, 9.29037e+01,\n",
+ " 9.29037e+01, 7.04972e+01, 7.04972e+01, 6.41238e+01, 6.41238e+01,\n",
+ " 6.36689e+01, 6.36689e+01, 6.32335e+01, 6.17085e+01, 6.17085e+01,\n",
+ " 6.12704e+01, 6.12704e+01, 5.46626e+01, 5.46626e+01, 5.34871e+01,\n",
+ " 5.34871e+01, 5.19145e+01, 5.19145e+01, 5.10759e+01, 5.10759e+01,\n",
+ " 4.91619e+01, 4.91619e+01, 4.81833e+01, 4.81833e+01, 3.96327e+01,\n",
+ " 3.96327e+01, 1.62620e+01, 1.62620e+01, 9.12295e+00, 9.12295e+00,\n",
+ " 9.12295e+00, 8.51678e+00, 8.51678e+00, 4.84582e+00, 4.84582e+00,\n",
+ " 4.59833e+00, 4.59833e+00, 4.58112e+00, 4.58112e+00, 4.51002e+00,\n",
+ " 4.51002e+00, 4.40158e+00, 4.40158e+00, 4.21667e+00, 4.21667e+00,\n",
+ " 4.13865e+00, 4.13865e+00, 4.00862e+00, 4.00862e+00, 3.90090e+00,\n",
+ " 3.90090e+00, 3.88944e+00, 3.88944e+00, 3.88786e+00, 3.88786e+00,\n",
+ " 3.88663e+00, 3.88663e+00, 3.88379e+00, 3.88379e+00, 3.87050e+00,\n",
+ " 3.87050e+00, 3.84187e+00, 3.84187e+00, 3.79809e+00, 3.79809e+00,\n",
+ " 3.78197e+00, 3.78197e+00, 3.70207e+00, 3.70207e+00, 3.58076e+00,\n",
+ " 3.58076e+00, 3.56106e+00, 3.56106e+00, 3.34893e+00, 3.34893e+00,\n",
+ " 3.06167e+00, 3.06167e+00, 1.75586e+00, 1.75586e+00, 1.75586e+00,\n",
+ " 1.58874e+00, 1.58874e+00, 1.30540e+00, 1.30540e+00, 1.29696e+00,\n",
+ " 1.27125e+00, 1.27125e+00, 1.24437e+00, 1.24437e+00, 8.29003e-01,\n",
+ " 8.29003e-01, 5.58009e-01, 5.58009e-01, 5.58009e-01, 4.61881e-01,\n",
+ " 4.61881e-01, 4.48474e-01, 4.48474e-01, 4.18709e-01, 4.18709e-01,\n",
+ " 4.18709e-01, 4.15897e-01, 4.15897e-01, 4.14227e-01, 4.14227e-01,\n",
+ " 4.13625e-01, 4.13625e-01, 4.13323e-01, 4.13323e-01, 4.12075e-01,\n",
+ " 4.12075e-01, 4.10658e-01, 4.10658e-01, 4.10523e-01, 4.10523e-01,\n",
+ " 4.09901e-01, 4.09901e-01, 4.09879e-01, 4.09879e-01, 4.09864e-01,\n",
+ " 4.09864e-01, 4.09698e-01, 4.09698e-01, 4.09389e-01, 4.09389e-01,\n",
+ " 4.08474e-01, 4.08474e-01, 4.06249e-01, 4.06249e-01, 4.04195e-01,\n",
+ " 4.04195e-01, 3.96060e-01, 3.96060e-01, 3.93015e-01, 3.90651e-01,\n",
+ " 3.90651e-01, 3.83317e-01, 3.83317e-01, 3.75523e-01, 3.75523e-01,\n",
+ " 3.74460e-01, 3.74460e-01, 3.73550e-01, 3.73550e-01, 3.71117e-01,\n",
+ " 3.71117e-01, 3.67281e-01, 3.67281e-01, 3.60663e-01, 3.60663e-01,\n",
+ " 3.54458e-01, 3.54458e-01, 3.49244e-01, 3.49244e-01, 3.46727e-01,\n",
+ " 3.46727e-01, 3.45318e-01, 3.45318e-01, 3.44284e-01, 3.44284e-01,\n",
+ " 3.41980e-01, 3.41980e-01, 3.35753e-01, 3.35753e-01, 3.35753e-01,\n",
+ " 3.35489e-01, 3.35489e-01, 3.29343e-01, 3.29343e-01, 3.29343e-01,\n",
+ " 3.26769e-01, 3.26769e-01, 3.19913e-01, 3.19913e-01, 3.18384e-01,\n",
+ " 3.18384e-01, 3.18198e-01, 3.18198e-01, 3.18103e-01, 3.18103e-01,\n",
+ " 3.17873e-01, 3.17873e-01, 3.17741e-01, 3.17741e-01, 3.16377e-01,\n",
+ " 3.16377e-01, 3.13142e-01, 3.13142e-01, 3.12609e-01, 3.12609e-01,\n",
+ " 3.12044e-01, 3.12044e-01, 3.11984e-01, 3.11984e-01, 3.06905e-01,\n",
+ " 3.06905e-01, 2.84829e-01, 2.84829e-01, 2.84787e-01, 2.84787e-01,\n",
+ " 2.84763e-01, 2.84763e-01, 2.84734e-01, 2.84734e-01, 2.82886e-01,\n",
+ " 2.82886e-01, 2.79732e-01, 2.79732e-01, 2.78938e-01, 2.78938e-01,\n",
+ " 2.78624e-01, 2.78624e-01, 2.78593e-01, 2.78593e-01, 2.78544e-01,\n",
+ " 2.78544e-01, 2.78507e-01, 2.78507e-01, 2.78341e-01, 2.78341e-01,\n",
+ " 2.78329e-01, 2.78329e-01, 2.78170e-01, 2.78170e-01, 2.78030e-01,\n",
+ " 2.78030e-01, 2.77994e-01, 2.77994e-01, 2.77945e-01, 2.77945e-01,\n",
+ " 2.77905e-01, 2.77905e-01, 2.77905e-01, 2.77905e-01, 2.77893e-01,\n",
+ " 2.77893e-01, 2.77826e-01, 2.77826e-01, 2.77565e-01, 2.77565e-01,\n",
+ " 2.77247e-01, 2.77247e-01, 2.76692e-01, 2.76692e-01, 2.76165e-01,\n",
+ " 2.76165e-01, 2.74387e-01, 2.74387e-01, 2.71900e-01, 2.71900e-01,\n",
+ " 2.65026e-01, 2.65026e-01, 2.62625e-01, 2.54725e-01, 2.54725e-01,\n",
+ " 2.54725e-01, 2.36225e-01, 2.36225e-01, 2.21180e-01, 2.21180e-01,\n",
+ " 2.06805e-01, 2.06805e-01, 1.99498e-01, 1.99498e-01, 1.98107e-01,\n",
+ " 1.98107e-01, 1.96974e-01, 1.96974e-01, 1.95735e-01, 1.95735e-01,\n",
+ " 1.94396e-01, 1.94396e-01, 1.92781e-01, 1.92781e-01, 1.91211e-01,\n",
+ " 1.91211e-01, 1.89741e-01, 1.89741e-01, 1.85730e-01, 1.85730e-01,\n",
+ " 1.85730e-01, 1.83799e-01, 1.83799e-01, 1.80274e-01, 1.80274e-01,\n",
+ " 1.80045e-01, 1.80045e-01, 1.79917e-01, 1.79917e-01, 1.78872e-01,\n",
+ " 1.78872e-01, 1.76442e-01, 1.76442e-01, 1.75316e-01, 1.75316e-01,\n",
+ " 1.74677e-01, 1.74677e-01, 1.74293e-01, 1.74293e-01, 1.73979e-01,\n",
+ " 1.73979e-01, 1.73979e-01, 1.73850e-01, 1.73850e-01, 1.73720e-01,\n",
+ " 1.73720e-01, 1.73691e-01, 1.73691e-01, 1.73677e-01, 1.73677e-01,\n",
+ " 1.73661e-01, 1.73661e-01, 1.73647e-01, 1.73647e-01, 1.73642e-01,\n",
+ " 1.73642e-01, 1.73637e-01, 1.73637e-01, 1.73631e-01, 1.73631e-01,\n",
+ " 1.73610e-01, 1.73610e-01, 1.73557e-01, 1.73557e-01, 1.73432e-01,\n",
+ " 1.73432e-01, 1.73432e-01, 1.73383e-01, 1.73383e-01, 1.73129e-01,\n",
+ " 1.73129e-01, 1.72543e-01, 1.72543e-01, 1.71775e-01, 1.71775e-01,\n",
+ " 1.71372e-01, 1.71372e-01, 1.70823e-01, 1.70823e-01, 1.70537e-01,\n",
+ " 1.70537e-01, 1.70537e-01, 1.70463e-01, 1.70463e-01, 1.70093e-01,\n",
+ " 1.70093e-01, 1.69113e-01, 1.69113e-01, 1.68421e-01, 1.68421e-01,\n",
+ " 1.68257e-01, 1.68257e-01, 1.68236e-01, 1.68236e-01, 1.68227e-01,\n",
+ " 1.68227e-01, 1.68220e-01, 1.68220e-01, 1.68211e-01, 1.68211e-01,\n",
+ " 1.68205e-01, 1.68205e-01, 1.68199e-01, 1.68199e-01, 1.68186e-01,\n",
+ " 1.68186e-01, 1.68170e-01, 1.68170e-01, 1.68153e-01, 1.68153e-01,\n",
+ " 1.68076e-01, 1.68076e-01, 1.67983e-01, 1.67983e-01, 1.67854e-01,\n",
+ " 1.67854e-01, 1.67691e-01, 1.67691e-01, 1.67691e-01, 1.67674e-01,\n",
+ " 1.67674e-01, 1.67648e-01, 1.67648e-01, 1.66781e-01, 1.66781e-01,\n",
+ " 1.65890e-01, 1.65890e-01, 1.65890e-01, 1.65363e-01, 1.65363e-01,\n",
+ " 1.65164e-01, 1.65164e-01, 1.64561e-01, 1.64561e-01, 1.64442e-01,\n",
+ " 1.64442e-01, 1.64333e-01, 1.64333e-01, 1.64304e-01, 1.64304e-01,\n",
+ " 1.64297e-01, 1.64297e-01, 1.64288e-01, 1.64288e-01, 1.64279e-01,\n",
+ " 1.64279e-01, 1.64276e-01, 1.64276e-01, 1.64274e-01, 1.64274e-01,\n",
+ " 1.64274e-01, 1.64274e-01, 1.64273e-01, 1.64273e-01, 1.64273e-01,\n",
+ " 1.64273e-01, 1.64273e-01, 1.64273e-01, 1.64273e-01, 1.64273e-01,\n",
+ " 1.64273e-01, 1.64273e-01, 1.64273e-01, 1.64273e-01, 1.64273e-01,\n",
+ " 1.64272e-01, 1.64272e-01, 1.64272e-01, 1.64272e-01, 1.64270e-01,\n",
+ " 1.64270e-01, 1.64268e-01, 1.64268e-01, 1.64265e-01, 1.64265e-01,\n",
+ " 1.64263e-01, 1.64263e-01, 1.64263e-01, 1.64262e-01, 1.64262e-01,\n",
+ " 1.64262e-01, 1.64262e-01, 1.64262e-01, 1.64262e-01, 1.64262e-01,\n",
+ " 1.64261e-01, 1.64261e-01, 1.64261e-01, 1.64261e-01, 1.64261e-01,\n",
+ " 1.64261e-01, 1.64260e-01, 1.64260e-01, 1.64260e-01, 1.64260e-01,\n",
+ " 1.64260e-01, 1.64259e-01, 1.64259e-01, 1.64259e-01, 1.64255e-01,\n",
+ " 1.64255e-01, 1.64244e-01, 1.64244e-01, 1.64244e-01, 1.64243e-01,\n",
+ " 1.64243e-01, 1.64235e-01, 1.64235e-01, 1.64210e-01, 1.64210e-01,\n",
+ " 1.64210e-01, 1.64209e-01, 1.64209e-01, 1.64201e-01, 1.64201e-01,\n",
+ " 1.64177e-01, 1.64177e-01, 1.64138e-01, 1.64138e-01, 1.64138e-01,\n",
+ " 1.64138e-01, 1.64138e-01, 1.64027e-01, 1.64027e-01, 1.64005e-01]), 'iterations': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
+ " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
+ " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n",
+ " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n",
+ " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n",
+ " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n",
+ " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n",
+ " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n",
+ " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n",
+ " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n",
+ " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n",
+ " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n",
+ " 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n",
+ " 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n",
+ " 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
+ " 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n",
+ " 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n",
+ " 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n",
+ " 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n",
+ " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n",
+ " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n",
+ " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n",
+ " 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n",
+ " 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n",
+ " 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n",
+ " 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n",
+ " 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n",
+ " 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,\n",
+ " 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377,\n",
+ " 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390,\n",
+ " 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403,\n",
+ " 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416,\n",
+ " 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429,\n",
+ " 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442,\n",
+ " 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455,\n",
+ " 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468,\n",
+ " 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481,\n",
+ " 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494,\n",
+ " 495, 496, 497, 498, 499, 500], dtype=uint32)})"
]
},
"execution_count": 19,
@@ -603,7 +748,7 @@
},
{
"cell_type": "markdown",
- "id": "regulation-foster",
+ "id": "featured-worthy",
"metadata": {},
"source": [
"### Plot the results"
@@ -611,7 +756,7 @@
},
{
"cell_type": "markdown",
- "id": "black-assets",
+ "id": "tested-conservation",
"metadata": {},
"source": [
"Plot the resiudal over steps to see how the calculation converges"
@@ -620,12 +765,12 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "partial-burner",
+ "id": "focused-occasion",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -643,7 +788,7 @@
},
{
"cell_type": "markdown",
- "id": "appreciated-bracelet",
+ "id": "amended-gamma",
"metadata": {},
"source": [
"Finally it is a good idea to have a look at the final potential. This can reveal unphysical behavior"
@@ -652,7 +797,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "demographic-marijuana",
+ "id": "comfortable-oasis",
"metadata": {},
"outputs": [
{
@@ -671,7 +816,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x1296 with 3 Axes>"
]
@@ -688,7 +833,7 @@
},
{
"cell_type": "markdown",
- "id": "permanent-convention",
+ "id": "baking-faith",
"metadata": {},
"source": [
"### Run a lammps simulations with the fitted potential"
@@ -697,36 +842,36 @@
{
"cell_type": "code",
"execution_count": 22,
- "id": "inclusive-brick",
+ "id": "quarterly-maryland",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job PotentialTest was saved and received the ID: 849\n",
- "The job strain_0_9 was saved and received the ID: 850\n",
- "The job strain_0_92 was saved and received the ID: 851\n",
- "The job strain_0_94 was saved and received the ID: 852\n",
- "The job strain_0_96 was saved and received the ID: 853\n",
- "The job strain_0_98 was saved and received the ID: 854\n",
- "The job strain_1_0 was saved and received the ID: 855\n",
- "The job strain_1_02 was saved and received the ID: 856\n",
- "The job strain_1_04 was saved and received the ID: 857\n",
- "The job strain_1_06 was saved and received the ID: 858\n",
- "The job strain_1_08 was saved and received the ID: 859\n",
- "The job strain_1_1 was saved and received the ID: 860\n",
- "job_id: 850 finished\n",
- "job_id: 851 finished\n",
- "job_id: 852 finished\n",
- "job_id: 853 finished\n",
- "job_id: 854 finished\n",
- "job_id: 855 finished\n",
- "job_id: 856 finished\n",
- "job_id: 857 finished\n",
- "job_id: 858 finished\n",
- "job_id: 859 finished\n",
- "job_id: 860 finished\n"
+ "The job PotentialTest was saved and received the ID: 56\n",
+ "The job strain_0_9 was saved and received the ID: 57\n",
+ "The job strain_0_92 was saved and received the ID: 58\n",
+ "The job strain_0_94 was saved and received the ID: 59\n",
+ "The job strain_0_96 was saved and received the ID: 60\n",
+ "The job strain_0_98 was saved and received the ID: 61\n",
+ "The job strain_1_0 was saved and received the ID: 62\n",
+ "The job strain_1_02 was saved and received the ID: 63\n",
+ "The job strain_1_04 was saved and received the ID: 64\n",
+ "The job strain_1_06 was saved and received the ID: 65\n",
+ "The job strain_1_08 was saved and received the ID: 66\n",
+ "The job strain_1_1 was saved and received the ID: 67\n",
+ "job_id: 57 finished\n",
+ "job_id: 58 finished\n",
+ "job_id: 59 finished\n",
+ "job_id: 60 finished\n",
+ "job_id: 61 finished\n",
+ "job_id: 62 finished\n",
+ "job_id: 63 finished\n",
+ "job_id: 64 finished\n",
+ "job_id: 65 finished\n",
+ "job_id: 66 finished\n",
+ "job_id: 67 finished\n"
]
}
],
@@ -741,12 +886,12 @@
{
"cell_type": "code",
"execution_count": 23,
- "id": "straight-honduras",
+ "id": "creative-mercury",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -764,13 +909,13 @@
{
"cell_type": "code",
"execution_count": 24,
- "id": "thousand-caribbean",
+ "id": "ranking-robertson",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "3.632164318463751"
+ "3.6284997615253496"
]
},
"execution_count": 24,
@@ -784,7 +929,7 @@
},
{
"cell_type": "markdown",
- "id": "impressed-exhaust",
+ "id": "complex-campbell",
"metadata": {},
"source": [
"### Same cane be done for the 1000 structures dataset\n",
@@ -799,17 +944,9 @@
{
"cell_type": "code",
"execution_count": 25,
- "id": "pretty-marina",
+ "id": "attempted-publication",
"metadata": {},
- "outputs": [
- {
- "name": "stdin",
- "output_type": "stream",
- "text": [
- "Are you sure you want to delete all jobs from 'PotentialDF2'? y/(n) y\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pr = Project(\"PotentialDF2\")\n",
"#pr.remove_jobs()\n",
@@ -822,7 +959,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "id": "proprietary-breed",
+ "id": "assisted-sheffield",
"metadata": {},
"outputs": [],
"source": [
@@ -837,7 +974,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "id": "convinced-gambling",
+ "id": "individual-vienna",
"metadata": {},
"outputs": [],
"source": [
@@ -847,17 +984,9 @@
{
"cell_type": "code",
"execution_count": 28,
- "id": "still-counter",
+ "id": "innocent-halifax",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The job PotentialDF2 was saved and received the ID: 861\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"j.input.atom_types.Cu = None\n",
"j.input.fit_algorithm = j.factories.algorithms.ar_lbfgs(max_iter=100000)\n",
@@ -873,13 +1002,13 @@
{
"cell_type": "code",
"execution_count": 29,
- "id": "photographic-division",
+ "id": "tender-certification",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "850.7202744483948"
+ "4.696846008300781e-05"
]
},
"execution_count": 29,
@@ -894,18 +1023,18 @@
{
"cell_type": "code",
"execution_count": 30,
- "id": "normal-newspaper",
+ "id": "confident-depression",
"metadata": {},
"outputs": [
{
"data": {
"application/json": {
"error": "None",
- "iterations": "array([ 1, 2, 3, ..., 5594, 5595, 5596], dtype=uint32)",
- "residual": "array([758.612 , 758.612 , 758.612 , ..., 58.7461, 58.7461, 58.7461])"
+ "iterations": "None",
+ "residual": "None"
},
"text/plain": [
- "Output({'error': None, 'residual': array([758.612 , 758.612 , 758.612 , ..., 58.7461, 58.7461, 58.7461]), 'iterations': array([ 1, 2, 3, ..., 5594, 5595, 5596], dtype=uint32)})"
+ "Output({'error': None, 'residual': None, 'iterations': None})"
]
},
"execution_count": 30,
@@ -919,7 +1048,7 @@
},
{
"cell_type": "markdown",
- "id": "liable-plain",
+ "id": "agreed-communications",
"metadata": {},
"source": [
"This is the result if the initilly guessed values are taken instead of the fitted ones."
@@ -928,17 +1057,9 @@
{
"cell_type": "code",
"execution_count": 31,
- "id": "approved-taxation",
+ "id": "bright-dependence",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The job PotentialDF2_BadStartParams was saved and received the ID: 862\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"j2 = pr.create_job(pr.job_type.Atomicrex, \"PotentialDF2_BadStartParams\")\n",
"j2.potential = job.potential.copy()\n",
@@ -954,18 +1075,18 @@
{
"cell_type": "code",
"execution_count": 32,
- "id": "brazilian-discovery",
+ "id": "cellular-blowing",
"metadata": {},
"outputs": [
{
"data": {
"application/json": {
"error": "None",
- "iterations": "array([ 1, 2, 3, ..., 5357, 5358, 5359], dtype=uint32)",
- "residual": "array([5717.28 , 5717.28 , 5717.28 , ..., 58.746, 58.746, 58.746])"
+ "iterations": "None",
+ "residual": "None"
},
"text/plain": [
- "Output({'error': None, 'residual': array([5717.28 , 5717.28 , 5717.28 , ..., 58.746, 58.746, 58.746]), 'iterations': array([ 1, 2, 3, ..., 5357, 5358, 5359], dtype=uint32)})"
+ "Output({'error': None, 'residual': None, 'iterations': None})"
]
},
"execution_count": 32,
@@ -979,7 +1100,7 @@
},
{
"cell_type": "markdown",
- "id": "passive-award",
+ "id": "tracked-drawing",
"metadata": {},
"source": [
"With this choice of functions and initial parameters starting directly from all structures gives the same residual. In a previous iteration of the potential it was about 7 times worse, so it is a good idea to test this."
@@ -1002,7 +1123,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.8"
+ "version": "3.8.6"
}
},
"nbformat": 4,
diff --git a/day_2/02-runner/Workflow-RuNNer.ipynb b/day_2/02-runner/Workflow-RuNNer.ipynb
index 2befa6aa6748772563f0f6f0f3a870c90b63a820..0c29dcbc362cece354ef04d3ea0992ed3446bfc8 100644
--- a/day_2/02-runner/Workflow-RuNNer.ipynb
+++ b/day_2/02-runner/Workflow-RuNNer.ipynb
@@ -1 +1,1505 @@
-{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Getting started with **RuNNer**\n## Constructing a HDNNP for Bulk Copper\n\nThis Jupyter Notebook is written for the RuNNer tutorial at the workshop \"WORKFLOWS FOR ATOMISTIC SIMULATION\" from 10-12 March, 2021 by **Marius Herbold** (marius.herbold@chemie.uni-goettingen.de, Georg-August-Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie).\n\nIt is written in text form for an easier understanding, if participants will get back later to this notebook. Anyhow, during the tutorial, we will\nnot explicitly go through the text.\n\nFor this tutorial it is intended to use the RuNNer release version 1.2.\nRuNNer is hosted at www.gitlab.com. The most recent version can only be found in this repository.\nFor access please contact Prof. Jörg Behler (joerg.behler@uni-goettingen.de)."},{"metadata":{"trusted":true},"cell_type":"code","source":"### Import python modules\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport ase\nfrom pyiron import Project\n\n### Import Marius Class and functions\nimport functions as fc\n\n### Varibales form RuNNer UC\nBohr2Ang = 0.5291772109030 # CODATA 2018\nAng2Bohr = 1/Bohr2Ang\nEh2eV = 27.211386245988 # CODATA 2018\neV2Eh = 1/Eh2eV\nf_conversion = eV2Eh/Ang2Bohr","execution_count":1,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# About RuNNer\n**RuNNer** is a stand-alone Fortran program for the construction of high-dimensional neural network potentials (HDNNP), written mainly by Jörg Behler. It relates the local environment of an atom to its atomic energy $E_\\mathrm{s}$, which contributes to the sum of all $N$ atomic energies, resulting in the total energy of the system $E_\\mathrm{s}$\n\n\\begin{equation}\n E_\\mathrm{s} = \\sum_{a}^{N}E_\\mathrm{a}.\n\\end{equation}\n\nThe atomic energy is described by an atomic neural network (NN), which is element specific. This gives the oppurtunity to describe different numbers of atoms with the same NN, which would be not the case, if there is only one NN for the whole system. To feed information to the NN, the local environment up to a certain cutoff radius $R_\\mathrm{c}$ is described by so-called symmetry functions (SF) (more details are shown in a few moments) forming the SF vector $G$, which forms the input layer of the NN. In the next layers of the NN - the hidden layers - this information will be processed and in the final layer - the output layer - the atomic NN will provide the atomic energy $E_\\mathrm{a}$. In each layer, there are a certain number of nodes $y$ which are connected by the weights $a$ and can be biased by the biases $b$. For the NN training the wheights and biases are optimized to represent best the data in the training data set.\n\n\n\nIn general **RuNNer** can be separated into three different stages - so-called modes, in which different steps are performed.\n- mode 1: SF calculation, data set splitting in training and test set\n- mode 2: training of the NN to construct the NNP\n- mode 3: prediction of energy, forces, stress, charges\n\nAll these steps are performed consecutively beginning with mode 1. Needed input files are:\n* ``input.nn``: \n - main control file needed in all modes\n - contains all control parameters (NN architecture, symmetry functios, ...)\n* ``input.data``:\n - needed in mode 1 and 3\n - contains structural information (lattice vectors, atomic positions, forces, charges, total energy)\n - output of electronic structure code must be converted to ``input.data`` format\n - RuNNer repository provides the RuNNerUC (universial converter) to convert from several formats (FHI-aims, VASP, xyz, LAMMPS) to input.data format and vice-versa"},{"metadata":{},"cell_type":"markdown","source":"# Getting the First Data Set\n\nBefore we are gettinger deeper into **RuNNer**, we will go one step back. At the beginning of each NNP, there is your data set. For sure, the data set does not have to be good/perfect/large, because you can increase your data set step by step and train different generations of your NN, ending up with an accurate potential. For getting your first data set, there are several ways like:\n- small random displacements,\n- thermal displacements by a simple potential - like force fields - in MD,\n- experimental structures.\n\nThe question \"What is a good data set?\" is not that simple to answer and it strongly depends on the purpose of your potential. But one important point is for sure the distribution of your data over the configurational space you like to handle with your potential. If some configurations are missing, the NNP will provide inaccurate results, because you make the NNP predict energies and forces for an unknown configuration. In **RuNNer**, this is called an ``extrapolation``, that means the NNP is not trained to such a configuration.\n\nHere in the workshop, we are dealing with bulk-Cu. So, a first application of your NNP could be to predict the equilibrium lattice constant of bulk-Cu and you will calculate the energy of a bulk-Cu unit cell with different lattice constants to give an energy-volume curve, which provides the equilibrium lattice constant at its minimum. Thus, your data set should contain information of different cell volumes."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Read in workshop's data set in pandas data frame\ndata_pr = Project(\"../../datasets\")\nif len(data_pr.job_table()) == 0:\n data_pr.unpack(\"Cu_training_archive\")\ndata_job = data_pr.load('df4_2_5eV_25A3_8K')\ndata = data_job.to_pandas()\nfig1 = fc.PlotData(data)","execution_count":2,"outputs":[{"output_type":"stream","text":"Number of points in plot: 8073\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 3686.4x2073.6 with 1 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already mentioned, your data needs to be stored in the ``input.data`` file. This file is used for ``mode 1`` to generate all the needed files for the NN training in ``mode 2``. In this case the ``input.data`` stores all the information of the electronic structure code like the total energy and charge, the structure (lattice constants, atomic positions), atomic forces and may the atomic charges. If used in ``mode 3``, only the structural paramteres are necessary, since ``mode 3`` is the prediction mode and we may do not know the outcome of an electronic structure calculation.\n\nThe ``input.data`` follows a certain format with certain keywords. Each structure is embedded between the keywords ``begin`` and ``end``, to separate different structures from each other. For periodic structures the three lattice vectors are introduced by the keyword ``lattice``, for non-periodic structures this keyword is just missing. Information about the atoms is given line by line, thus each atom in one line, beginning with the ``atom`` keyword, followed by the Cartesian coordinates (x, y and z), the element, the atomic charge, an unused column and the atomic force components (fx, fy and fz). The ``energy`` keyword is followed by the total energy of the current structure, equivalent, the overall charge is marked by the ``charge`` keyword. Comments can also be added with the ``comment`` keyword. Important aspect: the data is given in special units. A length is given in ``bohr``, an energy in ``Hartree``, thus forces in ``Hartree/bohr`` and charges in the elementary charge ``e``. In general, periodic and non-periodic structures can be mixed, as well as different numbers of atoms per structure can be combined. Information can be given in a free format (number of digits), but it is recommended to use at least six digits and the order of the keywords is arbitrary in general."},{"metadata":{},"cell_type":"raw","source":"begin\nlattice 2.34735543 -4.06574009 0.00000000\nlattice 2.34735543 4.06574009 0.00000000\nlattice 0.00000000 0.00000000 13.45504276\ncomment x y z element atomic charge unused fx fy fz\natom 0.00000000 0.00000000 0.00000000 Cu 0.00000000 0.00000000 -0.00000000 -0.00000000 0.00000002\natom 2.34735543 1.35524733 10.09128112 Cu 0.00000000 0.00000000 -0.00000000 0.00000134 -0.00000002\natom 0.00000000 0.00000000 6.72752138 Cu 0.00000000 0.00000000 0.00000000 0.00000000 -0.00000004\natom 2.34735543 -1.35524733 3.36375974 Cu 0.00000000 0.00000000 0.00000000 -0.00000134 0.00000003\nenergy -0.4746414926841609\ncharge 0.0\nend"},{"metadata":{},"cell_type":"markdown","source":"# The RuNNer Workflow\n\nWe discussed the ``input.data`` and will have a look at the next steps. Here, the ``input.nn`` is explained. Keywords can be given in an arbitrary order, but grouping keywords by the modes is useful for the general structure. The units are the same as for the ``input.data``, see above. If a keyword is not specified, **RuNNer** uses default values, if possible. A summary in the output files give more detailed information in the specific case. Most keywords can only be specified ones, to avoid contradictions, otherwise an error will be printed. Also comments can be added to the file, which are indicated by a hash ``#``. In principle, it is possible to change the ``input.nn`` for every mode, however it is highly recommended not to do that. Anyway here, you will not have the opportunity to explicitly change the ``input.nn``, since **RuNNer** is called via the pyiron environment. At the moment, the implementation of **RuNNer** to pyiron is on a very early stage, thus no changes are possible for the input.\n\nIn the following we will discuss a subset, but the most important keywords. Beginning with some general keywords of the ``input.nn``. I think the first keywords are self-explanatory together with the given comments. The data set splitting in ``mode 1`` and the initial weights in ``mode 2`` rely on random numbers. For the reproducibility, a random number seed (keyword ``random_seed``) has to be defined, which will give the same results, if the run is repeated later. Together with this, the generator for the random numbers can also be defined (keyword ``random_number_type``). The second group of keywords describe the architecture of the NN and the activation functions of the nodes via the keywords ``global_...``. Usually, we use 2-3 hidden layers with 10-40 nodes each."},{"metadata":{},"cell_type":"raw","source":"### general keywords\nnn_type_short 1 # 1=Behler-Parrinello energy is a sum of atomic energies\nrunner_mode 1 # 1=calculate symmetry functions, 2=fitting mode, 3=predicition mode\nnumber_of_elements 1 # number of elements\nelements Cu # specification of elements\nrandom_seed 20 # integer seed for random number generator\nrandom_number_type 6 # 6 recommended\n\n\n### NN structure of the short-range NN\nuse_short_nn # use NN for short range interactions\nglobal_hidden_layers_short 2 # number of hidden layers\nglobal_nodes_short 15 15 # number of nodes in hidden layers\nglobal_activation_short t t l # activation functions (t = hyperbolic tangent, l = linear)"},{"metadata":{},"cell_type":"markdown","source":"# Pyiron RuNNer Fit\n\nHere, you will not have the opportunity to explicitly change the ``input.nn``, since **RuNNer** is called via the pyiron environment. At the moment, the implementation of **RuNNer** to pyiron is on a very early stage, thus no changes are possible for the input."},{"metadata":{"trusted":true},"cell_type":"code","source":"pr = Project(\"runner_fit\")","execution_count":3,"outputs":[]},{"metadata":{"trusted":true,"scrolled":true},"cell_type":"code","source":"data_pr.job_table()","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":" id status chemicalformula job \\\n0 1 finished None df1_A1_A2_A3_EV_elast_phon \n1 2 finished None df3_10k \n2 3 finished None df2_1k \n3 4 finished None df4_2_5eV_25A3_8K \n\n subjob projectpath project \\\n0 /df1_A1_A2_A3_EV_elast_phon /home/jovyan/ datasets/Cu_database/ \n1 /df3_10k /home/jovyan/ datasets/Cu_database/ \n2 /df2_1k /home/jovyan/ datasets/Cu_database/ \n3 /df4_2_5eV_25A3_8K /home/jovyan/ datasets/Cu_database/ \n\n timestart timestop totalcputime computer \\\n0 2021-02-18 19:49:53.061360 None None zora@cmti001#1 \n1 2021-02-18 19:49:55.496691 None None zora@cmti001#1 \n2 2021-02-18 19:49:56.101883 None None zora@cmti001#1 \n3 2021-02-18 19:49:57.547918 None None zora@cmti001#1 \n\n hamilton hamversion parentid masterid \n0 TrainingContainer 0.4 None None \n1 TrainingContainer 0.4 None None \n2 TrainingContainer 0.4 None None \n3 TrainingContainer 0.4 None None ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>status</th>\n <th>chemicalformula</th>\n <th>job</th>\n <th>subjob</th>\n <th>projectpath</th>\n <th>project</th>\n <th>timestart</th>\n <th>timestop</th>\n <th>totalcputime</th>\n <th>computer</th>\n <th>hamilton</th>\n <th>hamversion</th>\n <th>parentid</th>\n <th>masterid</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>finished</td>\n <td>None</td>\n <td>df1_A1_A2_A3_EV_elast_phon</td>\n <td>/df1_A1_A2_A3_EV_elast_phon</td>\n <td>/home/jovyan/</td>\n <td>datasets/Cu_database/</td>\n <td>2021-02-18 19:49:53.061360</td>\n <td>None</td>\n <td>None</td>\n <td>zora@cmti001#1</td>\n <td>TrainingContainer</td>\n <td>0.4</td>\n <td>None</td>\n <td>None</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>finished</td>\n <td>None</td>\n <td>df3_10k</td>\n <td>/df3_10k</td>\n <td>/home/jovyan/</td>\n <td>datasets/Cu_database/</td>\n <td>2021-02-18 19:49:55.496691</td>\n <td>None</td>\n <td>None</td>\n <td>zora@cmti001#1</td>\n <td>TrainingContainer</td>\n <td>0.4</td>\n <td>None</td>\n <td>None</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>finished</td>\n <td>None</td>\n <td>df2_1k</td>\n <td>/df2_1k</td>\n <td>/home/jovyan/</td>\n <td>datasets/Cu_database/</td>\n <td>2021-02-18 19:49:56.101883</td>\n <td>None</td>\n <td>None</td>\n <td>zora@cmti001#1</td>\n <td>TrainingContainer</td>\n <td>0.4</td>\n <td>None</td>\n <td>None</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>finished</td>\n <td>None</td>\n <td>df4_2_5eV_25A3_8K</td>\n <td>/df4_2_5eV_25A3_8K</td>\n <td>/home/jovyan/</td>\n <td>datasets/Cu_database/</td>\n <td>2021-02-18 19:49:57.547918</td>\n <td>None</td>\n <td>None</td>\n <td>zora@cmti001#1</td>\n <td>TrainingContainer</td>\n <td>0.4</td>\n <td>None</td>\n <td>None</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"j = pr.create.job.RunnerFit('fit', delete_existing_job=False)","execution_count":5,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Be aware of fitting a larger data set, since it will run some time, roughly six hours!\nj.add_job_to_fitting(data_pr.load('df1_A1_A2_A3_EV_elast_phon'))","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"j.run()","execution_count":7,"outputs":[{"output_type":"stream","text":"The job fit was saved and received the ID: 82\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"j.lammps_potential","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":" Name \\\n0 RuNNer-Cu \n\n Filename \\\n0 [/home/jovyan/day_2/02-runner/runner_fit/fit_hdf5/fit/input.nn, /home/jovyan/day_2/02-runner/runner_fit/fit_hdf5/fit/weights.029.data, /home/jovyan/day_2/02-runner/runner_fit/fit_hdf5/fit/scaling.... \n\n Model Species \\\n0 RuNNer [Cu] \n\n Config \n0 [pair_style nnp dir \"./\" showew no showewsum 0 resetew no maxew 100 cflength 1.8897261328 cfenergy 0.0367493254 emap \"1:Cu\"\\n, pair_coeff * * 12\\n] ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Name</th>\n <th>Filename</th>\n <th>Model</th>\n <th>Species</th>\n <th>Config</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>RuNNer-Cu</td>\n <td>[/home/jovyan/day_2/02-runner/runner_fit/fit_hdf5/fit/input.nn, /home/jovyan/day_2/02-runner/runner_fit/fit_hdf5/fit/weights.029.data, /home/jovyan/day_2/02-runner/runner_fit/fit_hdf5/fit/scaling....</td>\n <td>RuNNer</td>\n <td>[Cu]</td>\n <td>[pair_style nnp dir \"./\" showew no showewsum 0 resetew no maxew 100 cflength 1.8897261328 cfenergy 0.0367493254 emap \"1:Cu\"\\n, pair_coeff * * 12\\n]</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"The data set, you will fit is a strongly reduced subset of the data shown above. For comparison, the same plot is shown here.\n\n**Be aware of fitting a larger data set, since it will run some time, roughly six hours!**"},{"metadata":{"trusted":true},"cell_type":"code","source":"df1_job = data_pr.load('df1_A1_A2_A3_EV_elast_phon')\ndf1 = df1_job.to_pandas()\nfig1 = fc.PlotData(df1)","execution_count":10,"outputs":[{"output_type":"stream","text":"Number of points in plot: 105\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 3686.4x2073.6 with 1 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RuNNer Mode 1\n\nIn **RuNNer**'s mode 1 the following steps are performed:\n- calculation of SF values,\n- splitting of data set in train and test data set.\n\nThe amount of test structures is defined by the keyword ``test_fraction``. Here, ``test_fraction 0.10`` means 10% of the data set will be used for testing and is not part of the training data. ``use_short_forces`` keyword states to use also the atomic forces for the fitting process in ``mode 2``, but it is recommended to use it also in ``mode 1`` to create the necessary force files."},{"metadata":{},"cell_type":"raw","source":"### symmetry function generation ( mode 1):\ntest_fraction 0.10000 # threshold for splitting between fitting and test set\nuse_short_forces # use forces and prepare the files in mode 1 for fitting in mode 2"},{"metadata":{},"cell_type":"markdown","source":"In the next group of keywords, the SFs are defined. There are two different types of SFs: the radial SFs ``symfunction_short XX type XX ...`` to describe the atomic distances and angular SFs ``symfunction_short XX type XX XX ...`` to describe the spatial distribution of the neighboring atoms. ``cutoff_type`` keyword describes the cutoff function type. All mentioned SF types are shown in the next section."},{"metadata":{},"cell_type":"raw","source":"### symmetry function definitions (all modes):\ncutoff_type 1\nsymfunction_short Cu 2 Cu 0.000000 0.000000 12.000000\nsymfunction_short Cu 2 Cu 0.006000 0.000000 12.000000\nsymfunction_short Cu 2 Cu 0.016000 0.000000 12.000000\nsymfunction_short Cu 2 Cu 0.040000 0.000000 12.000000\nsymfunction_short Cu 2 Cu 0.109000 0.000000 12.000000\n\nsymfunction_short Cu 3 Cu Cu 0.00000 1.000000 1.000000 12.000000\nsymfunction_short Cu 3 Cu Cu 0.00000 1.000000 2.000000 12.000000\nsymfunction_short Cu 3 Cu Cu 0.00000 1.000000 4.000000 12.000000\nsymfunction_short Cu 3 Cu Cu 0.00000 1.000000 16.000000 12.000000\nsymfunction_short Cu 3 Cu Cu 0.00000 -1.000000 1.000000 12.000000\nsymfunction_short Cu 3 Cu Cu 0.00000 -1.000000 2.000000 12.000000\nsymfunction_short Cu 3 Cu Cu 0.00000 -1.000000 4.000000 12.000000\nsymfunction_short Cu 3 Cu Cu 0.00000 -1.000000 16.000000 12.000000"},{"metadata":{},"cell_type":"markdown","source":"### Definition of the Symmetry Functions (SFs)\n\nDifferent types of SFs for the radial and angular SFs are implemented in **RuNNer**, but only the most common types are shown here. SFs provide the input for the NN and describe the local atomic environment of each atom and are rotationally and translationally invariant. So, SFs describe the relative positions of the atoms to each other. In contrast, Cartesian coordinates describe the absolute positions to each other and change with a translation or a rotation. That means the numerical input will change with translation or rotation, but not the energy of the system. However, different numerical inputs belonging to the same energy leads to problems in fitting."},{"metadata":{},"cell_type":"markdown","source":"### The Cutoff Function\n\nAnother kind of symmetry function is the cutoff function, which is included in the radial and angular SFs. The cutoff radius $R_\\mathrm{c}$ (usually $12\\,\\mathrm{bohr}$) defines how much of the local atomic environment is considered. All SFs will decrease to zero, if the atomic distance is larger than $R_\\mathrm{c}$. A decrease to exact zero is necessary for numerical reasons. There are several cutoff funtions defined in **RuNNer** and we will use here\n\n\\begin{equation}\n f_{c}(R_{ij}) = \n \\begin{cases}\n 1& ~ \\text{for $R_{ij} \\leq R_{inner,c}$}\\\\\n 0.5 * [cos(\\pi x) + 1]& ~ \\text{for $R_{inner,c} \\leq R_{ij} \\leq R_\\mathrm{c}$},\\\\\n 0& ~ \\text{for $R_\\mathrm{c} < R_{ij}$}\n \\end{cases}\n\\end{equation}\n\nwith the atomic distance $R_{ij}$, the cutoff radius $R_\\mathrm{c}$, the inner cutoff $R_{inner,c}$ (here $=0$) and $x = \\frac{R_{ij} - R_{inner,c}}{R_\\mathrm{c} - R_{inner,c}}$."},{"metadata":{"trusted":true},"cell_type":"code","source":"distances = np.arange(0,15.1,0.1)\ncfct = np.array([fc.cutofffct(i) for i in distances])\nplt.plot(distances, cfct);","execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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cekIXTzzkys6cn77Zvzi71+zrHC313FEgp4KXTwTFRnBn4f3Ii0xlnEz8tmx77DXkUSCmgpdPNW0UQwTR/SmZH8Z9768mIpKXSQVOVkqdPFct8xkfnttVz5ft4M/vr/a6zgiQUuFLgHhRl8WI/q1ZtLc9by9bKvXcUSCkgpdAsbDg88mt3UT/uvVpawp2ut1HJGgo0KXgBETFcHEEb1JiInirhn5lB4q9zqSSFBRoUtASU+K45lberF55wEeeGUJR45o+ToRf6nQJeD0bduUX1zVmQ9XbuepTwq8jiMSNFToEpBuPS+b63IzePzDNXyyarvXcUSCggpdApKZ8bvrutG5RRL3zVrMxh37vY4kEvBU6BKw4mMimTSyN2bGXTPyOVBW4XUkkYCmQpeAltU0gSeH57K6aC8P/e0rvFoyUSQYqNAl4F3YIY0HL+/InKVbeO5f33gdRyRgqdAlKNx94Vlc3iWd372zkvnrd3gdRyQgqdAlKEREGI/d1IM2zRIY/9Iitu456HUkkYCjQpeg0Tgumskje3OwrJK7Zy7icEWl15FEAooKXYJK++aNeeymHizZvJtfz9HydSI1qdAl6Azs2pK7v3cWLy/YxKwFm7yOIxIwVOgSlB68vCMDclJ5+I3lLNm82+s4IgFBhS5BKTLCeHJYLmmNY7l7Zj4lWr5ORIUuwSulUQyTRvZm5/4yxr+0SMvXSdhToUtQ65qRzG+v68b89Tv5/XurvI4j4qkorwOInKobemeyrHA3U+ZtoHtmE67u0crrSCKe0BG6hIRfXNUFX5sUfvLqMlZv0/J1Ep78KnQzG2hmq82swMweOs64PmZWaWY3NFxEkfrFREXwzC29SIyL4q4Zeew5qOXrJPzUW+hmFgk8DQwCugDDzazLMcb9Hni/oUOK+KN5UhzP3tKLwl0HtXydhCV/jtD7AgXOufXOuTJgFjCkjnH3An8DtLyMeMaX3ZSHr+7CR6u28+THa72OI3JG+VPoGcDmGq8Lq7f9m5llANcBE4/3hczsTjPLM7O84uLiE80q4peR/drw/V4ZPPHhWj5aWeR1HJEzxp9Ctzq21f6/7BPAT51zx50tyTk32Tnnc8750tLS/IwocmK+W77u7FZJ/OiVJXxTouXrJDz4U+iFQFaN15nAllpjfMAsM/sGuAF4xsyubYiAIicjLjqSiSN6ExlRtXzd/sNavk5Cnz+FvhDIMbO2ZhYDDAPm1BzgnGvrnMt2zmUDrwI/cM79vaHDipyIrKYJ/Hl4Lmu37+XHs5fqIqmEvHoL3TlXAYyn6u6VlcBs59xyMxtnZuNOd0CRUzEgJ43/c2Vn3lu+TRdJJeT59aSoc+4d4J1a2+q8AOqcu+3UY4k0nDH927Jy616e+HAtHdMbM6hbS68jiZwWelJUQp6Z8dvrutIzqwkPzF7Kyq2lXkcSOS1U6BIW4qIjmTyyN0nxUYydnscOTbcrIUiFLmGjeVIck0f6KN53mB+8uIhyTbcrIUaFLmGlR1YT/nB9d77csJP/fnO513FEGpSmz5Wwc21uBiu3lTLps/V0apHEiH5tvI4k0iB0hC5h6SdXdOKijmn8es5y5q/f4XUckQahQpewFBlhTBieS5tmCfzgxUVs3nnA60gip0yFLmErKS6aKaN8VB5x3D5tIaWHNIe6BDcVuoS1dmmJPDuiFxtK9nPPi1poWoKbCl3C3nlnpfLb67oyb20Jv5qzHOc054sEJ93lIgIM7dOa9SX7mfTZetqlJTKmf1uvI4mcMBW6SLWfXtGJb0r288jbK2jTNIFLu6R7HUnkhOiUi0i1iAjj8aE96doqmR/OWszyLXu8jiRyQlToIjUkxEQx9VYfyfHRjJ2eR1HpIa8jifhNhS5SS3pSHFNv9bHnYDljp+dxoEyrHUlwUKGL1OHsVsk8OSyXr7fs4f5Xlmi1IwkKKnSRY7i0Szo/v7Iz7y8v4nfvrPQ6jki9dJeLyHGM6d+WTTsPMPWfG2jVJJ7bdTujBDAVushxmBm/uvpsikoP8Zu3V9AiOY4rtYSdBCidchGpR2SEMWFYLr1ap/CjV5awYMNOryOJ1EmFLuKHuOhIpo7ykZkSzx0v5FGwfa/XkUSOokIX8VNKoximj+5LdGQEtz63UPeoS8BRoYucgKymCUwb3YfdB8q47fmF7NWUuxJAVOgiJ6hrRjLPjOjN2qK93DUjn0PllV5HEgFU6CIn5cIOafzvjT34fN0OfvjyYs2jLgFBhS5ykq7NzeDXV3fhHyuKeOi1r/Q0qXhO96GLnILbzm/L7oPlPPHhWprER/PzqzpjZl7HkjClQhc5RfddksPuA+VM/ecGUhrFcM9F7b2OJGFKhS5yisyMhwd3Yc/Bcv74/mqS4qMZ2a+N17EkDKnQRRpARITxhxu6s/dQOQ+/8TVJcVEM6ZnhdSwJM35dFDWzgWa22swKzOyhOvbfYmbLqn99bmY9Gj6qSGCLjozgqZt70Te7KQ/MXsrby7Z6HUnCTL2FbmaRwNPAIKALMNzMutQatgG40DnXHfgNMLmhg4oEg7joSJ67rQ+5WU24b9Zi3l++zetIEkb8OULvCxQ459Y758qAWcCQmgOcc58753ZVv5wPZDZsTJHg0Sg2iudH96FrRjLjX1rERyuLvI4kYcKfQs8ANtd4XVi97VjGAO/WtcPM7jSzPDPLKy4u9j+lSJBpHBfN9Nv70rllEnfPXMSnq7d7HUnCgD+FXtdNtXU+QWFmF1FV6D+ta79zbrJzzuec86WlpfmfUiQIJcdH88LtfWnfPJE7Z+Tzz7UlXkeSEOdPoRcCWTVeZwJbag8ys+7AVGCIc25Hw8QTCW5NEmKYOfYc2qU2YuwLC/linX405PTxp9AXAjlm1tbMYoBhwJyaA8ysNfAaMNI5t6bhY4oEr6aNqko9KyWB0dMW8NkanW6U06PeQnfOVQDjgfeBlcBs59xyMxtnZuOqhz0MNAOeMbMlZpZ32hKLBKHUxFhm3dmPtqmJ3DE9j3/o7hc5Dcw5byYU8vl8Li9PvS/hZc+BckY9v4Cvv93DE0N7cnWPVl5HkiBjZvnOOV9d+zTbosgZlJwQzcwxfendOoX7Zi3mr3mb6/8kET+p0EXOsO9uaTy/fSr/9eoyZnzxjdeRJESo0EU8EB8TydRbfVzaOZ1fvrGciZ+tw6vTnxI6VOgiHomNiuTZEb24ukcrHn13Ff/z1gotkiGnRLMtingoOjKCCUN70rxxLH/55wa2lx7msZt6EBcd6XU0CUIqdBGPRUQYvxzchZbJcTzy9kqK9x1mykgfyQnRXkeTIKNTLiIBYuyAdjw5PJfFm3Zx46TP2bL7oNeRJMio0EUCyDU9WjH99r5s3X2I7z/zOau2lXodSYKICl0kwJx3Vip/vftcHI4bnv2Cj1dp+l3xjwpdJAB1apHE3+85n+zUBMZMz2PyXN3WKPVToYsEqJbJ8cy+61wGdW3B795ZxYN/XcbhikqvY0kAU6GLBLCEmCieGt6LH12aw98WFTJ88ny27TnkdSwJUCp0kQAXEWH86NIOPHNLL1Zt28vgP8/j83VaLEOOpkIXCRJXdmvJG/ecT3J8NCOmfqnpAuQoKnSRIJKT3pg3xvdnUNeWPPruKu6akc/uA2Vex5IAoUIXCTKJsVE8dXMuvxzchU9Wb2fQhHnMX6+l7USFLhKUzIwx/dvy2t3nExcdyc1T5vOnf6ymovKI19HEQyp0kSDWLTOZt+7tz/W9Mnny4wJumvQF64v3eR1LPKJCFwlyjWKj+OONPXhyeC7rivczaMI8ps5bT6Wm4g07KnSREHFNj1Z8cP8FDMhJ5ZG3V+poPQyp0EVCSPOkOKaM8vH40B4UbN/HwAnzePKjtRwq1xOm4UCFLhJizIzrcjP54P4LuLxLOn/6YA0Dn5jL3DXFXkeT00yFLhKimifF8dTNvZg55hwizBj13AJ+8GI+m3ce8DqanCYqdJEQ1z8nlXd/NIAHL+/Ax6u2c8ljn/HIWyv0QFIIUqGLhIHYqEjGX5zDpw9exHW5GTz3rw1c8IdPmPTZOg6W6fx6qDCv5oLw+XwuLy/Pk/cWCXertpXy6Lur+HR1MamJMYwd0I4R/dqQGKtlhgOdmeU753x17lOhi4Svhd/s5MmP1jJvbQlNEqK5/fy2jOjXhqaNYryOJsegQheR41q8aRdPfVzAR6u2ExsVwTU9WnHredl0zUj2OprUokIXEb+s3raX6V98w+uLvuVgeSW+Ninc5MtiULcWNI6L9jqeoEIXkRO050A5f83fzItfbmJDyX5ioyK4rEs61+Vm0D8nldioSK8jhi0VuoicFOccizfv5u+Lv+XNpVvYdaCcRjGRDMhJ4+LOzbmoY3PSGsd6HTOsnHKhm9lAYAIQCUx1zj1aa79V778SOADc5pxbdLyvqUIXCS5lFUf4Z0ExH67czscrt7OttGpt047pjemdnUKf7BR8bZqSmRJPVSXI6XBKhW5mkcAa4DKgEFgIDHfOragx5krgXqoK/RxggnPunON9XRW6SPByzrFiaymfri5mwYadLNq4i72HKwBIiosiJ70xHdITyWnemMyUeJonxZGeFEtqYizRkXr85VQcr9D9uem0L1DgnFtf/cVmAUOAFTXGDAFecFX/Osw3syZm1tI5t/UUs4tIADIzzm6VzNmtkrnnIqg84lhTtJe8jbtYva2UtUX7eH95ES8v2Fzr8yA+OpK46EjioiKIjY4kKiL8juaH9sli7IB2Df51/Sn0DKDmd6WQqqPw+sZkAP9R6GZ2J3AnQOvWrU80q4gEqMgIo3PLJDq3TPqP7Tv2HWbrnkMUlR6iqPQwRaWH2He4gsMVlRwqP8Kh8kqOhOFC16mJp+e6gz+FXtc/n7W/A/6MwTk3GZgMVadc/HhvEQlizRJjaZYYq/vZzxB/TmYVAlk1XmcCW05ijIiInEb+FPpCIMfM2ppZDDAMmFNrzBxglFXpB+zR+XMRkTOr3lMuzrkKMxsPvE/VbYvPOeeWm9m46v0TgXeousOlgKrbFkefvsgiIlIXv6ZWc869Q1Vp19w2scbHDrinYaOJiMiJ0A2hIiIhQoUuIhIiVOgiIiFChS4iEiI8m23RzIqBjSf56alASQPGOR2U8dQFej4I/IyBng8CP2Og5WvjnEura4dnhX4qzCzvWJPTBAplPHWBng8CP2Og54PAzxjo+WrSKRcRkRChQhcRCRHBWuiTvQ7gB2U8dYGeDwI/Y6Dng8DPGOj5/i0oz6GLiMjRgvUIXUREalGhi4iEiKArdDMbaGarzazAzB7yOk9tZpZlZp+Y2UozW25m93mdqS5mFmlmi83sLa+z1KV6GcNXzWxV9Z/luV5nqsnM7q/+/n5tZi+bWVwAZHrOzLab2dc1tjU1sw/MbG317ykBmPGP1d/nZWb2upk1CaR8NfY9aGbOzFK9yOaPoCr06gWrnwYGAV2A4WbWxdtUR6kAfuyc6wz0A+4JwIwA9wErvQ5xHBOA95xznYAeBFBWM8sAfgj4nHNdqZpWepi3qQCYBgyste0h4CPnXA7wUfVrL03j6IwfAF2dc92pWpD+Z2c6VA3TODofZpYFXAZsOtOBTkRQFTo1Fqx2zpUB3y1YHTCcc1udc4uqP95LVRFleJvqP5lZJnAVMNXrLHUxsyTgAuAvAM65Mufcbk9DHS0KiDezKCCBAFihyzk3F9hZa/MQYHr1x9OBa89kptrqyuic+4dzrqL65XyqVjzzxDH+DAEeB35CHUtrBpJgK/RjLUYdkMwsG8gFvvQ4Sm1PUPWX84jHOY6lHVAMPF99WmiqmTXyOtR3nHPfAv9L1dHaVqpW6PqHt6mOKf271cOqf2/ucZ763A6863WImszsGuBb59xSr7PUJ9gK3a/FqAOBmSUCfwN+5Jwr9TrPd8xsMLDdOZfvdZbjiAJ6Ac8653KB/Xh/quDfqs9DDwHaAq2ARmY2wttUwc/Mfk7VKcsXvc7yHTNLAH4OPOx1Fn8EW6EHxWLUZhZNVZm/6Jx7zes8tZwPXGNm31B1yupiM5vpbaSjFAKFzrnv/mfzKlUFHyguBTY454qdc+XAa8B5Hmc6liIzawlQ/ft2j/PUycxuBQYDt7jAejjmLKr+4V5a/TOTCSwysxaepjqGYCt0fxas9pSZGVXnflc65/7kdZ7anHM/c85lOueyqfrz+9g5F1BHl865bcBmM+tYvekSYIWHkWrbBPQzs4Tq7/clBNBF21rmALdWf3wr8IaHWepkZgOBnwLXOOcOeJ2nJufcV8655s657OqfmUKgV/Xf0YATVIVefeHkuwWrVwKznXPLvU11lPOBkVQd+S6p/nWl16GC0L3Ai2a2DOgJ/M7bOP9f9f8cXgUWAV9R9XPk+ePhZvYy8AXQ0cwKzWwM8ChwmZmtpeoujUcDMONTQGPgg+qfl4nH/SJnPl/Q0KP/IiIhIqiO0EVE5NhU6CIiIUKFLiISIlToIiIhQoUuIhIiVOgiIiFChS4iEiL+HwHArYTo7KZ6AAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"### The Radial Symmetry Functions\n\nTo define the parameters for the radial SFs, it is important to know which are the shortest bonds in your data set. Usually, 5-6 radial SF are used for each element pair, with different $\\eta$ values to increase the resolution for structure description. It is possible to shift the maximum of the radial SF $G^2$ by $R_{s}$\n\n\\begin{equation}\n G_{i}^{2} = \\sum_{j}^{}e^{\\eta (R_{ij} - R_{s})^2} \\cdot f_{c}(R_{ij}).\n\\end{equation}\n\nBelow, the defintion of a radial SF in ``input.nn``, again ``symfunction_short`` calls to define a SF, ``Cu`` defines the specific element, ``2`` the SF type, the second ``Cu`` defines the neighboring atom, and the last three parameters define $\\eta$, $R_{s}$ and $R_\\mathrm{c}$. The gaussian exponent $\\eta$ for the radial SF are chosen to equally distribute the radial SF turning points, whereas the turning point of radial SF with $\\eta = 0$ is set to the specific minimum bond in your data set. There is no need to define element specific SF, also global SF are possible, which are used for every element combination. It is also possible to define for each SF a different $R_\\mathrm{c}$, but it is recommended to use only one $R_\\mathrm{c}$ for all SFs. "},{"metadata":{},"cell_type":"raw","source":"symfunction_short Cu 2 Cu 0.000000 0.000000 12.000000"},{"metadata":{},"cell_type":"markdown","source":"Here, different radial parts of radial SFs with different $\\eta$ are plotted. Feel free and play around with the parameters."},{"metadata":{"trusted":true},"cell_type":"code","source":"rsf1 = np.array([fc.radialSF(i, 0.1) for i in distances])\nrsf2 = np.array([fc.radialSF(i, 0.05) for i in distances])\nrsf3 = np.array([fc.radialSF(i, 0.025) for i in distances])\nplt.plot(distances, rsf1[:,1], label='');\nplt.plot(distances, rsf2[:,1], label='');\nplt.plot(distances, rsf3[:,1], label='');","execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"### The Angular Symmetry Functions\n\nFor the angular SF it is quite similar as for the radial SF. But here, three atomic positions are included.\n\n\\begin{equation}\n G_{i}^{3} = 2^{\\zeta - 1}\\sum_{j}^{} \\sum_{k}^{} \\left[( 1 + \\lambda \\cdot cos \\theta_{ijk})^{\\zeta} \\cdot e^{\\eta (R_{ij}^2 + R_{ik}^2 + R_{jk}^2)} \\cdot f_{c}(R_{ij}) \\cdot f_{c}(R_{ik}) \\cdot f_{c}(R_{jk}) \\right],\n\\end{equation}\n\nthe angle $\\theta_{ijk} = \\frac{\\mathbf{R}_{ij} \\cdot \\mathbf{R}_{ik}}{R_{ij} \\cdot R_{ik}}$ is centered at atom $i$ and the atomic distance vector is defined as $\\mathbf{R}_{ij} = \\mathbf{R}_{i} - \\mathbf{R}_{j}$. Mostly used for the angular exponent $\\zeta = 1, 2, 4 ,16$, gaussian exponent $\\eta = 0$ and for $\\lambda$ only $+1$ or $-1$ is possible. If many atoms of each element are present, angular SFs are usually not critical and a default set of SFs can be used.\n\nHere a definition of an angular SF is given, which is similar to the definition of a radial SF. ``3`` defines the used type of SF, which needs the following parameters: ``Cu Cu`` to describe the neighboring atoms included in the angle, followed by $\\eta$, $\\lambda$, $\\zeta$ and $R_\\mathrm{c}$."},{"metadata":{},"cell_type":"raw","source":"symfunction_short Cu 3 Cu Cu 0.00000 1.000000 1.000000 12.000000"},{"metadata":{},"cell_type":"markdown","source":"Below, you find different angular parts for angular SF with different $\\zeta$ and $\\lambda$ values. Fell free and play around."},{"metadata":{"trusted":true},"cell_type":"code","source":"angles = range(0,361)\nasf1 = np.array([fc.angularSF(i,1,1,1,0.0,1.0,1.0) for i in angles])\nasf2 = np.array([fc.angularSF(i,1,1,1,0.0,1.0,2.0) for i in angles])\nasf3 = np.array([fc.angularSF(i,1,1,1,0.0,1.0,4.0) for i in angles])\nasf4 = np.array([fc.angularSF(i,1,1,1,0.0,-1.0,1.0) for i in angles])\nasf5 = np.array([fc.angularSF(i,1,1,1,0.0,-1.0,2.0) for i in angles])\nasf6 = np.array([fc.angularSF(i,1,1,1,0.0,-1.0,4.0) for i in angles])\nplt.plot(angles, asf1[:,1]);\nplt.plot(angles, asf2[:,1]);\nplt.plot(angles, asf3[:,1]);\nplt.plot(angles, asf4[:,1]);\nplt.plot(angles, asf5[:,1]);\nplt.plot(angles, asf6[:,1]);","execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"### Output Mode 1\n\nAs said, the data set splitting and the calculation of the symmetry function values takes place here. Among general information of your data set and your SFs, you find explicitly how the data set is splitted. As shown in the example below, it is written what happens to each structure (called ``Point``) and if it goes to the training or test set and which number it has there. ``mode 1`` will prepare the necessary files for ``mode 2``:\n* training data\n - function.data: SF values for each atom in each structure\n - trainstruct.data: structural information\n - trainforces.data: force information (if force fitting is used)\n \n\n* test data\n - testing.data: SF values for each atom in each structure\n - teststruct.data: structural information\n - testforces.data: force information (if force fitting is used)"},{"metadata":{},"cell_type":"raw","source":" -------------------------------------------------------------\n Maximum number of atoms: 128\n -------------------------------------------------------------\n Calculating Symmetry Functions\n for 8073 structures\n -------------------------------------------------------------\n 1 Point is used for training 1\n 2 Point is used for training 2\n 3 Point is used for training 3\n 4 Point is used for testing 1\n 5 Point is used for training 4\n 6 Point is used for training 5"},{"metadata":{},"cell_type":"markdown","source":"**RuNNer** ends without any problems, if at the end of the ouput file the following lines are written:"},{"metadata":{},"cell_type":"raw","source":" Normal termination of RuNNer\n -------------------------------------------------------------"},{"metadata":{},"cell_type":"markdown","source":"### Short quick example of SF calculation\n\nTo clarify how the SFs represent the atomic environment for an atom. Let's have a look at this simple structure with three Cu atoms below:"},{"metadata":{},"cell_type":"raw","source":"begin\natom 0.00000000 0.00000000 0.00000000 Cu 0.00000000 0.00000000 -0.00000000 -0.00000000 0.00000002\natom 0.00000000 0.00000000 6.72752138 Cu 0.00000000 0.00000000 0.00000000 0.00000000 -0.00000004\natom 2.34735543 -1.35524733 3.36375974 Cu 0.00000000 0.00000000 0.00000000 -0.00000134 0.00000003\nenergy -0.4746414926841609\ncharge 0.0\nend"},{"metadata":{},"cell_type":"markdown","source":"Here we define the atomic positions as vectors, calculate the distances and angles for the SFs. Finally, we will end up with the SFs vector for the first Cu atom. We use the 13 SFs, which were introduced in the section above \"RuNNer Mode 1\"."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Define atomic positions as vectors\nd1 = np.array([0.0, 0.0, 0.0])\nd2 = np.array([0.0, 0.0, 6.72752138])\nd3 = np.array([2.34735543, 1.35524733, 3.36375974])\n\n# Define distance vectors\nd12 = d1 - d2\nd13 = d1 - d3\nd23 = d2 - d3\n\n# Define angles\na123 = np.dot(d12, d13) / (np.linalg.norm(d12) * np.linalg.norm(d13))\na213 = np.dot(d12, d23) / (np.linalg.norm(d12) * np.linalg.norm(d23))\na312 = np.dot(d13, d23) / (np.linalg.norm(d13) * np.linalg.norm(d23))\n\n# Calculate radial symmetry function values\nfor eta in [0.000, 0.006, 0.016, 0.040, 0.109]:\n value_sf = 0\n for d in [d12, d13]:\n d = np.linalg.norm(d)\n value_sf += fc.radialSF(d, eta)[0]\n print(value_sf)\n\n# Calculate angular symmetry function values\nfor Lambda in [1, -1]:\n for zeta in [1, 2, 4, 16]:\n for a in [a123]:\n value_sf = fc.angularSF(a, np.linalg.norm(d12), np.linalg.norm(d13), np.linalg.norm(d23), 0.0, Lambda, zeta)[0]\n print(value_sf)\n ","execution_count":14,"outputs":[{"output_type":"stream","text":"1.1182423115740479\n0.9463318326255568\n0.7253600030997174\n0.40425285668735783\n0.09616369636385737\n0.411992195819298\n0.4119731729121053\n0.41193512973271224\n0.41170694441910877\n1.9023785577599554e-05\n8.78384860610243e-10\n1.872667361071411e-18\n1.7583909381106493e-70\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## RuNNer Mode 2\n\nIn ``mode 2``, the magic happens and your data will be fitted. The part below of the ``input.nn`` defines how the fitting in ``mode 2`` has to take place. ``epochs`` define how often **RuNNer** will loop over the training data to optimize the weights and biases of the NN, ``fitting_unit`` defines in which unit the output will be presented in ``mode 2``, all other files and units will stay in ``bohr`` and ``Hartree``. ``precondition_weights`` effects the initial weights and biases of the NN. In the second part, there are some parameters for the Kalman-Filter, ``repeated_energy_update`` repeats the energy update after a force component update, to increase the impact of the energies. This is slower in general, but might be necessary, since there a many more force components than energies. ``mix_all_points`` mixes the order of the training points for each epoch to improve the training. Often, the ranges of the symmetry functions are rather different in their order of magnitude and thus a rescaling of SFs can be advantageous numerically stated by ``scale_symmetry_functions`` keyword. Together with that, a centering of the SF average value to zero is performed for numerical reasons, since zero is the non-linear center of most activations functions. ``short_force_fraction`` defines how much of the force components is randomly used for training the NN. The last part, defines to write certain files for each epoch, to analyze it in a later stage. There are many other keywords and options to present. However, you got an idea how **RuNNer** works and what to do to fit your first NNP. In the next part, first steps for analyzing the fit are presented."},{"metadata":{},"cell_type":"raw","source":"### fitting (mode 2):general inputs for short range AND electrostatic part:\n\nepochs 10 # number of epochs\nfitting_unit eV # unit for error output in mode 2 (eV or Ha)\nprecondition_weights # optional precondition initial weights\n\n\n### fitting options ( mode 2): short range part only:\n\nshort_energy_error_threshold 0.10000 # threshold of adaptive Kalman filter short E\nshort_force_error_threshold 1.00000 # threshold of adaptive Kalman filter short F\nkalman_lambda_short 0.98000 # Kalman parameter short E/F, do not change\nkalman_nue_short 0.99870 # Kalman parameter short E/F, do not change\nuse_short_forces # use forces for fitting\nrepeated_energy_update # optional: repeat energy update for each force update\nmix_all_points # do not change\nscale_symmetry_functions # optional\ncenter_symmetry_functions # optional\nshort_force_fraction 0.01 #\n\n\n### output options for mode 2 (fitting):\nwrite_trainpoints # write trainpoints.out and testpoints.out files\nwrite_trainforces"},{"metadata":{},"cell_type":"markdown","source":"During the fitting process of the NN, the error function $\\Gamma$ is minimized, which is defined as \n\\begin{equation}\n \\Gamma = \\frac{1}{N_\\mathrm{struct}} \\sum_{i}^{N_\\mathrm{struct}} (E_{NN}^{i} - E_{Ref}^{i})^2 = RMSE(E),\n\\end{equation}\nif only energy fitting is used, which defines simultaneously the root-mean squared error of the energies $RMSE(E)$. This defines the differences of the reference data and the NNP predictions. During the epochs, the error decreases as you can see in the part of ``mode2`` output."},{"metadata":{},"cell_type":"raw","source":" -------------------------------------------------------------------------------\n RMSEs (energies: eV/atom, forces: eV/Bohr):\n --- E_short: --- - time -\n /atom min\n epoch train test\n ENERGY 0 0.486020 0.481254 9.86\n FORCES 0 0.543702 0.502894\n -------------------------------------------------------------------------------\n ENERGY 1 0.039459 0.039840 19.05\n FORCES 1 0.201312 0.174885\n INFORMATION USED FOR UPDATE (E,F) 1 1998 45\n -------------------------------------------------------------------------------\n ENERGY 2 0.024635 0.026306 19.14\n FORCES 2 0.132738 0.123616\n INFORMATION USED FOR UPDATE (E,F) 2 5565 112\n -------------------------------------------------------------------------------\n ENERGY 3 0.022316 0.024581 19.13\n FORCES 3 0.120274 0.111427\n INFORMATION USED FOR UPDATE (E,F) 3 6033 131\n -------------------------------------------------------------------------------\n ENERGY 4 0.021333 0.023145 19.16\n FORCES 4 0.113496 0.105447\n INFORMATION USED FOR UPDATE (E,F) 4 6132 142\n -------------------------------------------------------------------------------\n ENERGY 5 0.022327 0.023597 19.13\n FORCES 5 0.113152 0.102596\n INFORMATION USED FOR UPDATE (E,F) 5 6064 137\n-------------------------------------------------------------------------------\n ENERGY 6 0.021007 0.022555 19.15\n FORCES 6 0.102685 0.094464\n INFORMATION USED FOR UPDATE (E,F) 6 6094 168\n -------------------------------------------------------------------------------\n ENERGY 7 0.021018 0.022213 19.15\n FORCES 7 0.098023 0.097181\n INFORMATION USED FOR UPDATE (E,F) 7 6226 158\n -------------------------------------------------------------------------------\n ENERGY 8 0.020692 0.022248 19.15\n FORCES 8 0.095995 0.097202\n INFORMATION USED FOR UPDATE (E,F) 8 6186 183\n -------------------------------------------------------------------------------\n ENERGY 9 0.020880 0.022219 19.16\n FORCES 9 0.094960 0.095833\n INFORMATION USED FOR UPDATE (E,F) 9 6122 176\n -------------------------------------------------------------------------------\n ENERGY 10 0.021217 0.022457 19.41\n FORCES 10 0.097554 0.094895\n INFORMATION USED FOR UPDATE (E,F) 10 6226 203\n =============================================================\n Best short range fit has been obtained in epoch 7\n --- E_short: --- --- F_short: ---\n train test train test\n OPTSHORT 0.021018 0.022213 0.098023 0.097181\n -------------------------------------------------------------\n max Eshort error in last epoch (train set): 0.281291 eV/atom (structure 788 )\n max Eshort error in last epoch (test set) : 0.261851 eV/atom (structure 253 )\n -------------------------------------------------------------\n Total runtime (s) : 12095.013\n Total runtime (min): 201.584\n Total runtime (h) : 3.360\n Normal termination of RuNNer\n -------------------------------------------------------------"},{"metadata":{},"cell_type":"markdown","source":"A first and simple plot to anlyze the progress of the fitting procedure, is to show the RMSEs over the epochs. Here, you can easily identify overfitting, if the training $RMSE$ is much lower than the test $RMSE$, for example.\nAnyhow, the $RMSE$ is a rather strong reduction of the really complex potential energy surface (PES) and can only be understood as a rule of thumb for the quality of the NNP fit."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Load here an example fit\n# Use results of the workshop participants\nfit2 = fc.RuNFit('runner_fit/fit_hdf5/fit', 9)\n#fit2 = fc.RuNFit('MH-df4-2', 7)\nfigRMSE = fit2.plot_rmse()","execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"For a more detailed analyze, you could have a look at the predicted energies and forces per structure. This is quite useful to identify inaccurately described structures. The training data set may has a specific order of different structures (bulk, slab, cluster, ...) and you can identify, if some parts of the data set are described inaccurately in general. The second plot shows the atomic energy prediction of the NNP over the reference values. For a perfect fit, all points will be on the blue line, but as we can see this is not the case. In this plot, we can identify, if some energies ranges are not well described in our data set. This is related to our first data set analysis above."},{"metadata":{"trusted":true},"cell_type":"code","source":"figE1, figE2, figF3 = fit2.plot_points()","execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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EcmhqAHk3nDJpTHAOndfD2BmdwGnAPEB5BTgdg/+fX06LHX0Aj4AxgOzANz9Y4J2mhbr06cPGzZsYPPmzVm/Ecmsc+fO9OnTp9jZEJEcihRA3P228L/8geGmte6+s5XX7g28Gfd6A3tKF+nS9AZ2AZuBW8zsCIIxKd9x9w8SL2Jms4HZAH379t0rEx07dqRfv37ZvwsRkTYqUhuImdUArxBUOV0HvGxm41t57WT1RYkV5anSdACOBK5395EEJZK92lAA3H2+u49299E9e/ZsTX5FRCRO1Cqsq4Hj3X0tgJkNBO4ERrXi2huAQ+Je9wE2RkzjwAZ3fybcfg8pAoiIiORH1F5YHRuDB4C7vwx0bOW1nwMGmFm/sHpsOnB/Qpr7gW+EvbGOBra5+yZ3/xvwppkNCtNNonnbiYiI5FnUEsgSM7sJuCN8fTqtnAvL3XeZ2XnAAqA9cLO7rzaz2nD/DQRzcJ0ErCPoRnxm3CnOB34TBp/1CftERCTPok5l0gk4FxhH0C7xGHBt2PupbCSbykRERNJr7VQmte7+U+CncSf8DsFYDBERaYOitoHMTLJtVg7zISIiZSZtCcTMTgO+DvQzs/gG7v2BLfnMmIiIlLZMVVhPAZuAHgRdeRttB1bkK1MiIlL60gYQd38deB3IeqJCERGpTFFHoh9tZs+Z2ftm9rGZ7Taz9/KdORERKV1RG9F/CZxGMJ3JvsA3gV/kK1MiIlL6onbjxd3XmVl7d99NMInhU3nMl4iIlLioAeTDcMT3cjO7iqBhvUv+siUiIqUuahXWGWHa8whmvj0E+Fq+MiUiIqUv0ziQ+cD/Ao+4+0fAR0B9ITImIiKlLVMJ5GbgCOAhM3vUzL4fPsBJRETauEzjQJ4GngZiZtYdOB74rpkNB5YRPJP8t/nPpoiIlJpMVVifcfe3Adx9C8FDpO4M940CTsx7DkVEpCRl6oX1gpmtJAgav3f3bY073H0prXwmiIiIlK9MbSC9gf8GjiV4Dvq9ZvYvZrZv/rMmIjkVixU7B1Jh0gYQd9/t7gvc/UyCrru3AFOB18zsNwXIn4jkSr06UEpuRR0HQvj0wReBl4D3gKH5ypRIxVNpQCpAxgBiZn3N7GIzex54gOD55ae4+8i8506kUhWqNBCLgVmwwJ51BTDJgbTPRA/nu+oN/A64y93L+oHieia6lAwzSPO3VzHXlIqQ6pnomUoglwFV7v69cg8eIkWn0oBUmEyN6Ivd3c1sYDgSfRWAmY0wsysKk0WRChGLBSWAxlJA43qhAkhdXWGuI21G1Eb0GwlKIzsB3H0FMD1fmRKRPFBJR3IsagDZz92fTdi2q7UXN7MTzWytma0zs0uT7DczmxfuX2FmRybsb29my8zsgdbmRaSgVBqQChA1gPzdzA4DHMDMphE8EyRrZtYeuBaYTNAl+DQzS+waPBkYEC6zgesT9n+HoFuxSHlRaUAqQNQAci7wP8BgM3sLuBCobeW1xwDr3H19OMbkLuCUhDSnALd74Gmgm5n1AjCzPsCXgF+1Mh8iIpKFSE8kdPf1wBfNrAvQzt235+DavYE3415vAI6KkKY3QennGuASYP90FzGz2QSlF/r27duqDIuIyB6RR6IDuPsHOQoeAJbsElHSmNkU4J1wQse03H2+u49299E9e/bMJp8iIpJEiwJIjm0gmF+rUR9gY8Q0Y4GTzayBoOrrn8zs1/nLqoiIJCpmAHkOGGBm/cxsH4JuwfcnpLkf+EbYG+toYJu7b3L3y9y9j7tXhcf9xd1nFDT3IiJtXKQ2EAAzOwaoij/G3W/P9sLuvsvMzgMWEMyvdbO7rzaz2nD/DcBDwEnAOuBD4MxsryciIrmVdi6spkRmdwCHAcuB3eFmd/cL8pe13NNcWCIiLZdqLqyoJZDRwFCPEm1ERKRNiNoGsgo4OJ8ZERGR8hK1BNIDeNHMngV2NG5095PzkisRESl5UQNILJ+ZEBGR8hN1JPrifGdERETKS9oAYmZPuPs4M9tO81HiRtAL64C85k5EREpW2gDi7uPCn2nnm5K9XXghLF9e7FxIvIatDVR1qyp2NkSKoroarrkmt+cs5kh0kYJ6fWtDsbMgUlEij0SXlsl1pJfWs/qJLKrTUCaRXFEJRCpabFEMqzesPpjYuXE9tihW3IxJ3ul3nH+RpjJpShw8D+Qjd9+dMXEJ0lQmbZvVG64SSJuh33fupJrKJG0JxMzamdnXzexBM3sHWANsMrPVZjbXzAbkK8MiIlLaMlVhLSSYRPEy4GB3P8TdDwKOBZ4GrjQzTaMuZaFuQl2xsyB5pirLwkpbhWVmHd19Z9oTREhTKlSFJdJ2qAord7Kdjbe9mZ0LfA5YCdzk7rviE5RL8BARkdzKVIV1G8FU7iuBycDVec+RiEgOqMoy/zKVQIa6+3AAM7sJeDb/WRIRab1YTazYWah4mUogTdVTiVVXIiLStmUqgRxhZu+F6wbsG77WZIoiIm1c2hKIu7d39wPCZX937xC3ruAhzcVixc6BiBSQpjKR3KmvL3YORKSAMo1E325m74U/t8e9/tDM1CbSFqhUISIpZKrC2j+uymp/4LPAj4G/AT8vRAalCGpq9qynK1XU1AQBxixYYM+6Ao9IxYtUhWVm3cwsBrwA7A983t2/29qLm9mJZrbWzNaZ2aVJ9puZzQv3rzCzI8Pth5jZQjN7KZyX6zutzYvEWRzxCcaLFweBwj1YYM9PBRCRipepCquHmf0X8DywCxjp7le4+5bWXtjM2gPXEgxQHAqcZmZDE5JNBgaEy2zg+nD7LuC77j4EOBo4N8mxEkWqL/pSKFXk43rx51SQE2mVTHNhfQBsBm4Btifud/efZn1hsy8AMXc/IXx9WXjO/4pL8z/AIne/M3y9Fqhx900J57oP+KW7/zndNTUXVhJmQamhpiZ1ySP+M5Iq3aGHwuuv7729rm7PF3Us1rIv7ca85VL8OfNxfpEKlO1cWHOBxr+wXD8XvTfwZtzrDcBREdL0BpoCiJlVASOBZ5JdxMxmE5Re6Nu3b2vzXLkWLdqznvglGyVdvFTb6+v1X79IBcnUiB5z9/pUSyuvbUm2JX7rpE1jZl2B3wMXuvt7SdLi7vPdfbS7j+7Zs2fWma0oLWn4rivwfEL5aJRPdc5cnV+kjcpUhfXvaY51d/9R1hduZRWWmXUEHgAWRK1KUxVWEslKCzU1zUsaqaRKF19VFYsl78kVX7XVkry1lqqwpFS0tEq3iFJVYWUKIMl6WnUBzga6u3vXVmSoA/AyMAl4C3gO+Lq7r45L8yXgPOAkguqtee4+xsyMYKbgd939wqjXVABJopBfoi29lgKIVLIy+vxl9Uhbd7+6cQHmA/sCZwJ3Af1bk6FwcsbzgAXAS8Bv3X21mdWaWW2Y7CFgPbAOuBH4drh9LHAG8E9mtjxcTmpNftqU+P96Cl1F1RK5yluqKqwJE3JzfpG2yt3TLsCngf8AXgNiwIGZjinVZdSoUS4ejNoohrq63KRpzXWL9d5F3IPP4p6RU3uWfHzucwhY4km+UzNVYc0FvkpQ+rjW3d/Pd0DLJ1VhhUq56KxqK2kryuizmFUVFvBdgulLrgA2hvNgNc6NlbTXk5SoUppyJNtr5iKvpVxlJ1JmMrWBtHP3fX3PnFjxU7trOnfJTmKvrKjBrSWz/abruivplUnPoLJXAf/MZKrC6pqp2ipKmlKhKqxQsYvO6a6f7b5sryd70/2SBNlWYd1nZleb2Xgz6xJ3sv5mdraZLQBOzHVmJYdK5b/JbKvQSqnqTUSayVSFNQl4FPhXYLWZbTOzLcCvgYOBme5+T/6zKVlLNYgv16IEgsRZe933Pi4xb1GPS6cCqgryToFaspC2CqvStMkqrAjVEbFFMWI1sbxfJ6u0uTiu1JT6CORKuc+SM1lVYZnZjLj1sQn7zstd9iSnWvjfZP3iiI3TufrSy7ZEUCklCT36VypEpjaQi+LWf5Gw76wc50VyJRfVPslk23sqWf6yUcr/tVeSSgnUkneZAoilWE/2WspIbFEMqzesPvg1Nq7HFsXCBLEIJ4nlJ1BVmliMWKymfNoYSjFPUpIydeN93t2PTFxP9roctMk2kAj17VZveF3C56CxHjzqbLqqN0/NDIux5x7rXkmZybYb7+DwWeQr49YbXw/KS04lt1r732S2vackPf2XLxUgUwAZAnwZmBK33vhazyCvEHUTwi//1nTl1Bdic7EYsYlBtaDFgk1N1YR1E9SQLhWhxd14zWyKuz+Qp/zkVZuswspWsmqWUu9+WqoSq7DCbarGknKRbRVWMj/MQX4kn/L1Ja/g0ToarCcVJpsAot5X+Zbr7rbZUJtG7tTVBdWE6rUmFSabKqwx7v5snvKTV2VThdXa6g1Vj5Q+/Y6kjLSqCsvMDjKzr5jZucAwMxtjZtmUXiRLTeMzUiaIqXqknKiEJxUg01QmE8MZdx8EJgO9CHpfXQGsNLN6M9NzQXIhQwDION1IrqtHFHjyS/dXKkCUR9r+wt3fSLKvA0F33vbu/vv8ZTF3yrkKK+lgvxYcn4s8iEjblG0V1l+TBQ8Ad9/l7veWS/AoRxmnG0klWfWI/uMVkRzLFECuKEgu2roUo7pjNTG8zptKHo3rTVOvpwoKybZH6ZmldhQRaYHIc2FVgqJWYaUbhBehuijtfFVRtLRKqrVVWDU1sGhR9seLSMlo7VxYictKM1uRg0ydaGZrzWydmV2aZL+Z2bxw/wozOzLqsSUl1YSELdA03UhLr1usEsXixfm/hogUl7unXIDVwKGplnTHZlqA9sCrQH9gH+AFYGhCmpOA/yUYvHg08EzUY5Mto0aN8qJo7BMVr65uz/b4pa4u/bmyOS7Z9TPJlI9MWno9ESlZwBJP8p2aqQTysbu/nmppZewaA6xz9/Xu/jFwF3BKQppTgNvD9/A00M3MekU8tvgaSwCN4ksAsRixhXUt73ZbqNHMac6XshG/piZ5iaemJrd5E5GSkCmAPJnHa/cG3ox7vSHcFiVNlGMBMLPZZrbEzJZs3ry51ZmOLMJzNCI/Sjbb68d/mUPOqq9S5nvRouTBTW0hIhUpUwAZ2LhiZpfl+NrJ5tRKbLVNlSbKscFG9/nuPtrdR/fs2bOFWWyFxpJC88wk/wLPxzPCNe+SiORZpgDSI2791BxfewNwSNzrPsDGiGmiHFt88V/WjV1zk43tsPrMYzsynT/PWjwmZcKEguVNRIokWcNI4wI8n2w9FwvQAVgP9GNPQ/jhCWm+RPNG9GejHptsKVgj+oQJmRu66+qcWNjQnO8G59Y2iCdoyreItAlk2Yje38zuN7M/xq03La0MXLuA84AFwEvAb919tZnVmlltmOyhMFCsA24Evp3u2NbkJ6cWL25efdW4Hl9iiDqwryWljJYMLGzJ/lKnB12JFEWmgYRp6yHcvaw6+xdkIGG6MR/x99qMWA3EFiWkiWtkb2oAT/M7aibbwX8tPC62KLZnNHwpaOl9EpEWSTWQMPLzQMysJ4C7F7ArU27lNYC0drBgst9Dpi/GxP+8CxRASk6xA4hKQFLhshqJHo4ErzOzvwNrgJfNbLOZ/Xu+MlqREntCJb6Ol6r7bbIuuPX12Y82L/d5r1pyn/ItF0+AFClHyRpGGhdgDvBnoF/ctv4EbQ9z0h1bikvBGtGTNaAnNqLHN5wnNK7vdZ7E9MnOkSpNsvMmbivzUeN1NRT3PZT5/RPJhBSN6JkCyDKgR5LtPYFl6Y4txaUgASTVVCOJgSRVz6j4L6NkASTdVCbpvsjSBaAy/wIkVoQAku1UNCJlKFUA6ZChgNLR3f+epNSy2cw65qgQVFnq6/duCHdP386QqQ49vpoJ9pw/V20XmQYkFqNaq6XXLfQjYuPzV+5tSCJZyno693Kc6r0gvbASv0yiBJD4evxEjcckOz7TF1dLGvbjg15LrpEvGa4bWxRLOqVK3YS6wvcQUwCRCpeqET1TFdZu4L0ky3ZgZ7pjS3HJWxVWlGqrdNUbyaqtki3JrhtVlDaUqMcVQgveb9EHNqraSioc2QwkdPf27n5AkmV/d1cVVqPEeadSTeMRP6lgql5QkLynViEfU1usHlqZrluqvZ3KpeeaSK4liyqNC9A13f6oaUplyVUJpG5hXYoddXv/59zS//JT9dRqrXS9sNIppRJIiryk/H2ISE6Q5VQm95nZ1WY23sy6NG40s/5mdraZLQBOzFt0K1H1i+tTP3c8SVtC0gkH05VAGuWyYThZfsvhP+cIpaGSGhUv0oZkHIluZicBpwNjgQOBXcBa4EHgJnf/W74zmSutakSvqoKGBiB8PnmM1I3a8Y2qNTXYxMV7P8882XGlqJR6YZXyfRKpYNk+Ex13f8jdT3f3Knf/lLt3d/dj3P3H5RQ8WivW7/Xm05nHgkASi9Xs/R8y7Pnya2z3aHyd7YSHxVKsfJXq/RCRJpmmMpkRtz42Yd95+cpUKYotAve6oOQBeCxYYvWLg6qmhOqm2OL65gHHgtexZE/zq6vbMy1Jygyk2VdoxcpLocd6iEhakceBJI77aBPjQKqq4PW9H/1uMaJXYZFQ5ZWqGibThIClVH1TSnkRkbzLtgrLUqwne115GhqSdqmtm5DiP+HE/5AbG4AbJTYE19S0/rnlURrsRUTyIFMA8RTryV63GbGaWPpxGY37wvEhTQEncWzHokXJz9NYnRVlPEbC2Ihko7NbLWpeRKRNyVSF9SHB0wANOCxcJ3zd3927pDq2FOWqF1ZWMk1pkm0VVrLqsnQ9vrIRf41iP3tDRAou2yqsIcCXgSlx642vh+Y6kyWtNcED9pQ0UjUEt6SBOKE0EJtozRvsw3VVZ4lIXiUbXdi4EJZQWpumVJaCPQ8kW+lGhkeZR8u9aV6oVo/OjjotvYhUPLIcib7QzM43s77xG81sHzP7JzO7DZiZl8jWFuWwG2+r20IS5/dKtq72j+Z0PwpH97okZAogJxLMyHunmW00sxfNbD3wCnAa8DN3vzXPeZR0Eqq+UvYQy5b+UKMr1ckeK5HudUnIOJVJU8LgAVI9gH+4+9Z8ZipfCvI8kCLKyzMyzJrP71WsqU3KgcbHFI7udUGlakSPHEAqQaUHkHg5642lP9T0Uj20K9UDuiR7utdFk/VcWHnKzKfN7M9m9kr488AU6U40s7Vmts7MLo3bPtfM1pjZCjP7v2bWrWCZbws05iO6VG1Fule5p3tdcooSQIBLgUfdfQDwaPi6GTNrD1wLTCboMnyamTV2Hf4zMMzdRwAvA5cVJNdlpFVtIfpDFZEIihVATgFuC9dvA6YmSTMGWOfu6939Y+Cu8Djc/WF33xWmexrok9/slh89I6MINNlj4ehel4RiBZDPuPsmgPDnQUnS9AbejHu9IdyW6Czgf3OeQwnoDzU6ldAKR/e6JHTI14nN7BHg4CS7Lo96iiTbmrXmmtnlBA+4+k2afMwGZgP07ds3VTJJRX+oIpJC3gKIu38x1T4ze9vMern7JjPrBbyTJNkG4JC4132AjXHnmEkwpcokT9OVzN3nA/Mh6IXVsnchIiKpFKsK6372jGCfCdyXJM1zwAAz62dm+wDTw+MwsxOB7wMnu/uHBciviIgkKFYAuRI4zsxeAY4LX2NmnzWzhwDCRvLzgAXAS8Bv3X11ePwvgf2BP5vZcjO7odBvQESkrctbFVY67r4FmJRk+0bgpLjXDwEPJUn3ubxmUEREMipWCURERMqcAoiIiGRFAURERLKiACIiIllRABERkawogIiISFYUQEREJCsKICIikhUFEBERyYoCiIiIZEUBREREsqIAIiIiWVEAERGRrBRlNt624MILYfnyYudCRCRQXQ3XXJPbc6oEIiIiWVEJJE9yHelFREqNSiAiIpIVBRCRHIstihU7CyIFoQAikmP1i+uLnQWRglAAERGRrCiAiORAbFEMqzes3gCa1lWdJZXM3L3YeSiY0aNH+5IlS4qdDalwVm94Xdv5u5LKZ2ZL3X104naVQEREJCtFCSBm9mkz+7OZvRL+PDBFuhPNbK2ZrTOzS5Ps/56ZuZn1yH+uRaKpm1BX7CyIFESxSiCXAo+6+wDg0fB1M2bWHrgWmAwMBU4zs6Fx+w8BjgPeKEiORSKK1cSKnQWRgihWADkFuC1cvw2YmiTNGGCdu69394+Bu8LjGv0MuARQZbOISBEUK4B8xt03AYQ/D0qSpjfwZtzrDeE2zOxk4C13fyHfGRURkeTyNheWmT0CHJxk1+VRT5Fkm5vZfuE5jo+Yj9nAbIC+fftGvLSIiGSStwDi7l9Mtc/M3jazXu6+ycx6Ae8kSbYBOCTudR9gI3AY0A94wcwatz9vZmPc/W9J8jEfmA9BN95s34+IiDRXrCqs+4GZ4fpM4L4kaZ4DBphZPzPbB5gO3O/uK939IHevcvcqgkBzZLLgISIi+VOUgYRm1h34LdCXoBfVqe7+rpl9FviVu58UpjsJuAZoD9zs7j9Ocq4GYLS7/z3CdTcDr2eZ7R5AxmuI7lNEuk/R6D5Fk+/7dKi790zc2KZGoreGmS1JNhJTmtN9ikb3KRrdp2iKdZ80El1ERLKiACIiIllRAIlufrEzUCZ0n6LRfYpG9ymaotwntYGIiEhWVAIREZGsKICIiEhWFEDimNmPzGyFmS03s4fDcSmJaQaF+xuX98zswnBfpGnqy12U+xSm62Zm95jZGjN7ycy+EG6PmdlbcffwpMK+g8LIwX3S56l5ugYzWxmmWxK3XZ+n5ulS3aecf57UBhLHzA5w9/fC9QuAoe5emyZ9e+At4Ch3f93MrgLedfcrw+eXHOju3y9I5gso6n0ys9uAx939V+FsAvu5+1YziwHvu/t/FzTjBZaD+6TPU/N0DSQZNKzP017pGkh+n3L+eVIJJE7jLyfUhcxTxU8CXnX3xtHtUaapL3tR7pOZHQCMB24Kj/nY3bcWJIMlIgf3SZ8naZKD+5Tzz5MCSAIz+7GZvQmcDvx7huTTgTvjXkeZpr4iRLhP/YHNwC1mtszMfmVmXeL2nxcWx2+u1KoZaPV90uepOQceNrOlFsyyHU+fpz1S3aecf57aXBWWpZlm3t3vi0t3GdDZ3ZM+nzSsatgIHO7ub4fbtrp7t7g0/8/dy/LD3Nr7ZGajgaeBse7+jJn9HHjP3X9gZp8hmLfHgR8Bvdz9rHy9l3zK833S56n5OT7r7hvN7CDgz8D57v6YPk97nSPVfcr958ndtSRZgEOBVWn2nwI8nLBtLcGHF6AXsLbY76NY9yn8I2iIe30s8GCSdFXp7nOlLNncJ32e0qaLAd9Lsr1Nf57S3ad8fJ5UhRXHzAbEvTwZWJMm+Wk0r76CaNPUl70o98mD6fXfNLNB4aZJwIvh8b3ikn4FWJWnrBZVa+8T+jzFp+liZvs3rhM8UG5V+Fqfpz1pUt4n8vF5KnYkLaUF+H14s1cAfwR6h9s/CzwUl24/YAvwqYTjuwOPAq+EPz9d7PdU5PtUDSwJ091L0OsD4A5gZbj9fsL/iiptycF90ucpvE8EbUUvhMtqgiqdxuP1eYp2n3L+eWpzbSAiIpIbqsISEZGsKICIiEhWFEBERCQrCiAiIpIVBRAREcmKAohUJDPbbc1nTb40bt89ZtY/XG+cuXSlmb1oZv9hZp3CfVVm9o+E89TFrcdf4wIzO8/MzkzIxxfM7MY0+awys6/n6R7Umtk3cnCeRWZ2QsK2C83sutaeW8pbh2JnQCRP/uHu1YkbzexwoL27r4/bPNHd/25mXQkeDTqfPQOuXk1ynvrwXO/H7zOz/YAngVvi0p4I/ClNPquArwP/J/Nbahl3vyFHp7qTYN63BXHbpgMX5+j8UqZUApG25nRSjMB19/eBWmCqmX26pSd29w+BBjMbE7d5EvBIWNJ43MyeD5djwv1XAseGpZg5ZtbZzG4JS0TLzGwigJnNMrN7zeyPZvZaWNq5KEzzdLL8WvCcjO+F64vM7Cdm9qyZvWxmxyZ7D2Z2sZk9F05MWB9uvgeYEl8yIxi89kRL75FUFgUQqVT7JlQ9/Uu4fSywNNVBHkyZ/RrQOG3EYXHnuDbCdZcQzGeFmfUAdrr7NuAd4Dh3PxL4F2BemP5SgmeBVLv7z4Bzw3wMJ5gu5zYz6xymHUZQWhkD/Bj40N1HAn8FolRVdXD3McCFQLJJ+I4P3/cYgtHxo8xsvLtvAZ4lKE1BUPq42zUKuc1TFZZUqqRVWASTyG3OcKzFrSerwkrnHWBwuH488HC43hH4pZlVA7uBgSmOHwf8AsDd15jZ63FpF7r7dmC7mW0jmM4Cgmk8RkTI2x/Cn0sJqs4SHR8uy8LXXQkCymPsqca6L/xZlrPdSm4pgEhb8w+gc6qd4UR0VcDLwKeyOH/n8BoAk4GfhutzgLeBIwhK/h+lykKac++IW/8k7vUnRPtbbky/O0V6A/7L3f8nyb57gZ+a2ZHAvu7+fITrSYVTFZa0NS8Bn0u2I2xEvw64193/X5bnHwisMjMjKBUsD7d/Ctjk7p8AZwDtw+3bgf3jjn+MoJ0GMxsI9CWYhrsQFgBnhfcBM+sdPlOisX1oEXAze89CLW2UAohUqsQ2kCvD7Q8CNQlpF5rZKoJ6/jeAf23FdccCjwCjgGVx7QTXATPN7GmCIPNBuH0FsMvMXjCzOWG69ma2ErgbmOXuOygAd3+YoDfYX8Pr30Pz4HYnQQnqrkLkR0qfZuOVNsXM9gUWEjwBcHeOzz0SuMjdzzCzK4B17q4vW6lYCiDS5oSD4l5y9zdyfN7jgFfcvSGX5xUpVQogIiKSFbWBiIhIVhRAREQkKwogIiKSFQUQERHJigKIiIhk5f8DOocxgP0hDQoAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"The same plot can also be done for the atomic forces."},{"metadata":{"trusted":true},"cell_type":"code","source":"figF1, figF2, figF3 = fit2.plot_forces()","execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"The ``mode 2`` output gives further information about the parameters of the fitting procedure, about the fitting data and it gives information about the defined SFs and the SFs ranges. These ranges define the known configuration space of your NNP and are used to identify the already mentioned ``extrapolations``. If the NNP is made to predict energies, forces or stress, it first calculates the SF vectors and compares these values to the trainings range shown below. If a SF value occurs, which is not in the range, **RuNNer** will give an ``extrapolation warning`` and tell the user, but you will still get your wanted energy, force or stress. Another usage of these ranges is to increase the data set and the known configuration space."},{"metadata":{},"cell_type":"raw","source":" =============================================================\n Short range symmetry function values for element Cu\n Training set: min max average range stddev range/stddev\n 1 6.72393532 25.25447208 14.00001115 18.53053676 4.02011080 4.60945921\n 2 5.03976308 19.37796546 10.59714429 14.33820238 3.11321693 4.60559053\n 3 3.25577537 13.11073890 6.98744718 9.85496353 2.14525332 4.59384607\n 4 1.25883453 6.21227127 3.05195579 4.95343674 1.07630994 4.60224009\n 5 0.06568766 1.53442885 0.48718005 1.46874120 0.26713619 5.49809898\n 6 2.42989188 42.06637435 12.30898440 39.63648248 7.96564228 4.97593051\n 7 6.59284339 109.34982696 33.28285935 102.75698358 21.03610544 4.88479124\n 8 0.78142670 19.11390444 5.22756080 18.33247774 3.61016236 5.07802030\n 9 5.20079617 86.39735705 26.20143575 81.19656088 16.68008353 4.86787495\n 10 0.15497639 7.72411752 1.91198556 7.56914113 1.46079520 5.18152107\n 11 3.59136462 60.38094318 17.95854981 56.78957856 11.72399989 4.84387403\n 12 0.00059457 1.66933490 0.34750355 1.66874033 0.32743124 5.09646040\n 13 0.52030958 18.24849080 4.70826264 17.72818122 3.59567219 4.93042198\n -------------------------------------------------------------"},{"metadata":{},"cell_type":"markdown","source":"During ``mode 2`` several files will be printed by **RuNNer** and some are printed every epoch:\n- scaling.data: information about the SFs if ``scale_symmetry_functions`` is used\n- xxxxxx.XXX.out: weights and biases of the specific epoch xxxxxx for atomic NN of element XXX\n- optweights.XXX.out: weights and biases of the epoch with lowest RMSE defined by **RuNNer** for element XXX\n- trainpoints.xxxxxx.out, testpoints.xxxxxx.out: optional, giving information about training and test energies of epoch xxxxxx\n- trainforces.xxxxxx.out, testforces.xxxxxx.out: optional, giving information about training and test forces of epoch xxxxxx"},{"metadata":{},"cell_type":"markdown","source":"## RuNNer Mode 3\n\n**RuNNer** ``mode 3`` is the prediction mode and brings the NNP to application. Via N2P2, NNP can also be used in LAMMPS. For ``mode 3``, the ``input.nn``, ``scaling.data`` (if scaling is used), ``weights.XXX.data`` and the ``input.data``, contaning the structures to predict, are needed. A first application of the NNP in the Cu case is to predict the correct energy-volume behaviour."},{"metadata":{"trusted":true},"cell_type":"code","source":"pr_ev = pr.create_group(\"E_V_curve\") # Creating a new sub-project within the main project\na_list = np.linspace(3.4, 4.0, 7)\nfor a in a_list:\n job_name = \"job_a_{:.4}\".format(a).replace(\".\", \"_\")\n job = pr_ev.create.job.Lammps(job_name, delete_existing_job=False)\n job.structure = pr_ev.create_ase_bulk(\"Cu\", a=a)\n #job.potential = '2012--Mendelev-M-I--Cu--LAMMPS--ipr1'\n job.potential = j.lammps_potential\n job.calc_minimize()\n job.run()","execution_count":18,"outputs":[{"output_type":"stream","text":"The job job_a_3_4 was saved and received the ID: 83\nThe job job_a_3_5 was saved and received the ID: 84\nThe job job_a_3_6 was saved and received the ID: 85\nThe job job_a_3_7 was saved and received the ID: 86\nThe job job_a_3_8 was saved and received the ID: 87\nThe job job_a_3_9 was saved and received the ID: 88\nThe job job_a_4_0 was saved and received the ID: 89\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"pr_ev.job_table()","execution_count":19,"outputs":[{"output_type":"execute_result","execution_count":19,"data":{"text/plain":" id status chemicalformula job subjob projectpath \\\n0 83 finished Cu job_a_3_4 /job_a_3_4 /home/jovyan/ \n1 84 finished Cu job_a_3_5 /job_a_3_5 /home/jovyan/ \n2 85 finished Cu job_a_3_6 /job_a_3_6 /home/jovyan/ \n3 86 finished Cu job_a_3_7 /job_a_3_7 /home/jovyan/ \n4 87 finished Cu job_a_3_8 /job_a_3_8 /home/jovyan/ \n5 88 finished Cu job_a_3_9 /job_a_3_9 /home/jovyan/ \n6 89 finished Cu job_a_4_0 /job_a_4_0 /home/jovyan/ \n\n project timestart \\\n0 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 07:32:12.843419 \n1 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 07:32:13.759829 \n2 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 07:32:14.706468 \n3 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 07:32:15.705833 \n4 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 07:32:16.604464 \n5 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 07:32:17.534287 \n6 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 07:32:18.524329 \n\n timestop totalcputime \\\n0 2021-03-09 07:32:13.347096 0.0 \n1 2021-03-09 07:32:14.211568 0.0 \n2 2021-03-09 07:32:15.184423 0.0 \n3 2021-03-09 07:32:16.130043 0.0 \n4 2021-03-09 07:32:17.055248 0.0 \n5 2021-03-09 07:32:18.072337 0.0 \n6 2021-03-09 07:32:19.045336 0.0 \n\n computer hamilton \\\n0 pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1 Lammps \n1 pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1 Lammps \n2 pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1 Lammps \n3 pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1 Lammps \n4 pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1 Lammps \n5 pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1 Lammps \n6 pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1 Lammps \n\n hamversion parentid masterid \n0 0.1 None None \n1 0.1 None None \n2 0.1 None None \n3 0.1 None None \n4 0.1 None None \n5 0.1 None None \n6 0.1 None None ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>status</th>\n <th>chemicalformula</th>\n <th>job</th>\n <th>subjob</th>\n <th>projectpath</th>\n <th>project</th>\n <th>timestart</th>\n <th>timestop</th>\n <th>totalcputime</th>\n <th>computer</th>\n <th>hamilton</th>\n <th>hamversion</th>\n <th>parentid</th>\n <th>masterid</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>83</td>\n <td>finished</td>\n <td>Cu</td>\n <td>job_a_3_4</td>\n <td>/job_a_3_4</td>\n <td>/home/jovyan/</td>\n <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n <td>2021-03-09 07:32:12.843419</td>\n <td>2021-03-09 07:32:13.347096</td>\n <td>0.0</td>\n <td>pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1</td>\n <td>Lammps</td>\n <td>0.1</td>\n <td>None</td>\n <td>None</td>\n </tr>\n <tr>\n <th>1</th>\n <td>84</td>\n <td>finished</td>\n <td>Cu</td>\n <td>job_a_3_5</td>\n <td>/job_a_3_5</td>\n <td>/home/jovyan/</td>\n <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n <td>2021-03-09 07:32:13.759829</td>\n <td>2021-03-09 07:32:14.211568</td>\n <td>0.0</td>\n <td>pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1</td>\n <td>Lammps</td>\n <td>0.1</td>\n <td>None</td>\n <td>None</td>\n </tr>\n <tr>\n <th>2</th>\n <td>85</td>\n <td>finished</td>\n <td>Cu</td>\n <td>job_a_3_6</td>\n <td>/job_a_3_6</td>\n <td>/home/jovyan/</td>\n <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n <td>2021-03-09 07:32:14.706468</td>\n <td>2021-03-09 07:32:15.184423</td>\n <td>0.0</td>\n <td>pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1</td>\n <td>Lammps</td>\n <td>0.1</td>\n <td>None</td>\n <td>None</td>\n </tr>\n <tr>\n <th>3</th>\n <td>86</td>\n <td>finished</td>\n <td>Cu</td>\n <td>job_a_3_7</td>\n <td>/job_a_3_7</td>\n <td>/home/jovyan/</td>\n <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n <td>2021-03-09 07:32:15.705833</td>\n <td>2021-03-09 07:32:16.130043</td>\n <td>0.0</td>\n <td>pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1</td>\n <td>Lammps</td>\n <td>0.1</td>\n <td>None</td>\n <td>None</td>\n </tr>\n <tr>\n <th>4</th>\n <td>87</td>\n <td>finished</td>\n <td>Cu</td>\n <td>job_a_3_8</td>\n <td>/job_a_3_8</td>\n <td>/home/jovyan/</td>\n <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n <td>2021-03-09 07:32:16.604464</td>\n <td>2021-03-09 07:32:17.055248</td>\n <td>0.0</td>\n <td>pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1</td>\n <td>Lammps</td>\n <td>0.1</td>\n <td>None</td>\n <td>None</td>\n </tr>\n <tr>\n <th>5</th>\n <td>88</td>\n <td>finished</td>\n <td>Cu</td>\n <td>job_a_3_9</td>\n <td>/job_a_3_9</td>\n <td>/home/jovyan/</td>\n <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n <td>2021-03-09 07:32:17.534287</td>\n <td>2021-03-09 07:32:18.072337</td>\n <td>0.0</td>\n <td>pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1</td>\n <td>Lammps</td>\n <td>0.1</td>\n <td>None</td>\n <td>None</td>\n </tr>\n <tr>\n <th>6</th>\n <td>89</td>\n <td>finished</td>\n <td>Cu</td>\n <td>job_a_4_0</td>\n <td>/job_a_4_0</td>\n <td>/home/jovyan/</td>\n <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n <td>2021-03-09 07:32:18.524329</td>\n <td>2021-03-09 07:32:19.045336</td>\n <td>0.0</td>\n <td>pyiron@jupyter-pyiron-2dpyiron-5fpotentialfit-2d5rxuppx2#1</td>\n <td>Lammps</td>\n <td>0.1</td>\n <td>None</td>\n <td>None</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_volume(job):\n return job[\"output/generic/volume\"][-1]\ndef get_energy(job):\n return job[\"output/generic/energy_tot\"][-1]","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Analysing the data\nvol_list = list()\nenergy_list = list()\n\nfor job in pr[\"E_V_curve\"].iter_jobs(status=\"finished\"):\n vol_list.append(get_volume(job))\n energy_list.append(get_energy(job))\n\nargs = np.argsort(vol_list)\nvol_list = np.array(vol_list)\nenergy_list = np.array(energy_list)\nplt.plot(vol_list[args], energy_list[args], \"-x\")\nplt.xlabel(\"Volume [$\\mathrm{\\AA^3}$]\")\nplt.ylabel(\"Energy [eV]\");","execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}
\ No newline at end of file
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Getting started with **RuNNer**\n",
+ "## Constructing a HDNNP for Bulk Copper\n",
+ "\n",
+ "This Jupyter Notebook is written for the RuNNer tutorial at the workshop \"WORKFLOWS FOR ATOMISTIC SIMULATION\" from 10-12 March, 2021 by **Marius Herbold** (marius.herbold@chemie.uni-goettingen.de, Georg-August-Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie).\n",
+ "\n",
+ "It is written in text form for an easier understanding, if participants will get back later to this notebook. Anyhow, during the tutorial, we will\n",
+ "not explicitly go through the text.\n",
+ "\n",
+ "For this tutorial it is intended to use the RuNNer release version 1.2.\n",
+ "RuNNer is hosted at www.gitlab.com. The most recent version can only be found in this repository.\n",
+ "For access please contact Prof. Jörg Behler (joerg.behler@uni-goettingen.de)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### Import python modules\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import ase\n",
+ "from pyiron import Project\n",
+ "\n",
+ "### Import Marius Class and functions\n",
+ "import functions as fc\n",
+ "\n",
+ "### Varibales form RuNNer UC\n",
+ "Bohr2Ang = 0.5291772109030 # CODATA 2018\n",
+ "Ang2Bohr = 1/Bohr2Ang\n",
+ "Eh2eV = 27.211386245988 # CODATA 2018\n",
+ "eV2Eh = 1/Eh2eV\n",
+ "f_conversion = eV2Eh/Ang2Bohr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# About RuNNer\n",
+ "**RuNNer** is a stand-alone Fortran program for the construction of high-dimensional neural network potentials (HDNNP), written mainly by Jörg Behler. It relates the local environment of an atom to its atomic energy $E_\\mathrm{s}$, which contributes to the sum of all $N$ atomic energies, resulting in the total energy of the system $E_\\mathrm{s}$\n",
+ "\n",
+ "\\begin{equation}\n",
+ " E_\\mathrm{s} = \\sum_{a}^{N}E_\\mathrm{a}.\n",
+ "\\end{equation}\n",
+ "\n",
+ "The atomic energy is described by an atomic neural network (NN), which is element specific. This gives the oppurtunity to describe different numbers of atoms with the same NN, which would be not the case, if there is only one NN for the whole system. To feed information to the NN, the local environment up to a certain cutoff radius $R_\\mathrm{c}$ is described by so-called symmetry functions (SF) (more details are shown in a few moments) forming the SF vector $G$, which forms the input layer of the NN. In the next layers of the NN - the hidden layers - this information will be processed and in the final layer - the output layer - the atomic NN will provide the atomic energy $E_\\mathrm{a}$. In each layer, there are a certain number of nodes $y$ which are connected by the weights $a$ and can be biased by the biases $b$. For the NN training the wheights and biases are optimized to represent best the data in the training data set.\n",
+ "\n",
+ "\n",
+ "\n",
+ "In general **RuNNer** can be separated into three different stages - so-called modes, in which different steps are performed.\n",
+ "- mode 1: SF calculation, data set splitting in training and test set\n",
+ "- mode 2: training of the NN to construct the NNP\n",
+ "- mode 3: prediction of energy, forces, stress, charges\n",
+ "\n",
+ "All these steps are performed consecutively beginning with mode 1. Needed input files are:\n",
+ "* ``input.nn``: \n",
+ " - main control file needed in all modes\n",
+ " - contains all control parameters (NN architecture, symmetry functios, ...)\n",
+ "* ``input.data``:\n",
+ " - needed in mode 1 and 3\n",
+ " - contains structural information (lattice vectors, atomic positions, forces, charges, total energy)\n",
+ " - output of electronic structure code must be converted to ``input.data`` format\n",
+ " - RuNNer repository provides the RuNNerUC (universial converter) to convert from several formats (FHI-aims, VASP, xyz, LAMMPS) to input.data format and vice-versa"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Getting the First Data Set\n",
+ "\n",
+ "Before we are gettinger deeper into **RuNNer**, we will go one step back. At the beginning of each NNP, there is your data set. For sure, the data set does not have to be good/perfect/large, because you can increase your data set step by step and train different generations of your NN, ending up with an accurate potential. For getting your first data set, there are several ways like:\n",
+ "- small random displacements,\n",
+ "- thermal displacements by a simple potential - like force fields - in MD,\n",
+ "- experimental structures.\n",
+ "\n",
+ "The question \"What is a good data set?\" is not that simple to answer and it strongly depends on the purpose of your potential. But one important point is for sure the distribution of your data over the configurational space you like to handle with your potential. If some configurations are missing, the NNP will provide inaccurate results, because you make the NNP predict energies and forces for an unknown configuration. In **RuNNer**, this is called an ``extrapolation``, that means the NNP is not trained to such a configuration.\n",
+ "\n",
+ "Here in the workshop, we are dealing with bulk-Cu. So, a first application of your NNP could be to predict the equilibrium lattice constant of bulk-Cu and you will calculate the energy of a bulk-Cu unit cell with different lattice constants to give an energy-volume curve, which provides the equilibrium lattice constant at its minimum. Thus, your data set should contain information of different cell volumes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of points in plot: 8073\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 3686.4x2073.6 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Read in workshop's data set in pandas data frame\n",
+ "data_pr = Project(\"../../datasets\")\n",
+ "if len(data_pr.job_table()) == 0:\n",
+ " data_pr.unpack(\"Cu_training_archive\")\n",
+ "data_job = data_pr.load('df4_2_5eV_25A3_8K')\n",
+ "data = data_job.to_pandas()\n",
+ "fig1 = fc.PlotData(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As already mentioned, your data needs to be stored in the ``input.data`` file. This file is used for ``mode 1`` to generate all the needed files for the NN training in ``mode 2``. In this case the ``input.data`` stores all the information of the electronic structure code like the total energy and charge, the structure (lattice constants, atomic positions), atomic forces and may the atomic charges. If used in ``mode 3``, only the structural paramteres are necessary, since ``mode 3`` is the prediction mode and we may do not know the outcome of an electronic structure calculation.\n",
+ "\n",
+ "The ``input.data`` follows a certain format with certain keywords. Each structure is embedded between the keywords ``begin`` and ``end``, to separate different structures from each other. For periodic structures the three lattice vectors are introduced by the keyword ``lattice``, for non-periodic structures this keyword is just missing. Information about the atoms is given line by line, thus each atom in one line, beginning with the ``atom`` keyword, followed by the Cartesian coordinates (x, y and z), the element, the atomic charge, an unused column and the atomic force components (fx, fy and fz). The ``energy`` keyword is followed by the total energy of the current structure, equivalent, the overall charge is marked by the ``charge`` keyword. Comments can also be added with the ``comment`` keyword. Important aspect: the data is given in special units. A length is given in ``bohr``, an energy in ``Hartree``, thus forces in ``Hartree/bohr`` and charges in the elementary charge ``e``. In general, periodic and non-periodic structures can be mixed, as well as different numbers of atoms per structure can be combined. Information can be given in a free format (number of digits), but it is recommended to use at least six digits and the order of the keywords is arbitrary in general."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "begin\n",
+ "lattice 2.34735543 -4.06574009 0.00000000\n",
+ "lattice 2.34735543 4.06574009 0.00000000\n",
+ "lattice 0.00000000 0.00000000 13.45504276\n",
+ "comment x y z element atomic charge unused fx fy fz\n",
+ "atom 0.00000000 0.00000000 0.00000000 Cu 0.00000000 0.00000000 -0.00000000 -0.00000000 0.00000002\n",
+ "atom 2.34735543 1.35524733 10.09128112 Cu 0.00000000 0.00000000 -0.00000000 0.00000134 -0.00000002\n",
+ "atom 0.00000000 0.00000000 6.72752138 Cu 0.00000000 0.00000000 0.00000000 0.00000000 -0.00000004\n",
+ "atom 2.34735543 -1.35524733 3.36375974 Cu 0.00000000 0.00000000 0.00000000 -0.00000134 0.00000003\n",
+ "energy -0.4746414926841609\n",
+ "charge 0.0\n",
+ "end"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# The RuNNer Workflow\n",
+ "\n",
+ "We discussed the ``input.data`` and will have a look at the next steps. Here, the ``input.nn`` is explained. Keywords can be given in an arbitrary order, but grouping keywords by the modes is useful for the general structure. The units are the same as for the ``input.data``, see above. If a keyword is not specified, **RuNNer** uses default values, if possible. A summary in the output files give more detailed information in the specific case. Most keywords can only be specified ones, to avoid contradictions, otherwise an error will be printed. Also comments can be added to the file, which are indicated by a hash ``#``. In principle, it is possible to change the ``input.nn`` for every mode, however it is highly recommended not to do that. Anyway here, you will not have the opportunity to explicitly change the ``input.nn``, since **RuNNer** is called via the pyiron environment. At the moment, the implementation of **RuNNer** to pyiron is on a very early stage, thus no changes are possible for the input.\n",
+ "\n",
+ "In the following we will discuss a subset, but the most important keywords. Beginning with some general keywords of the ``input.nn``. I think the first keywords are self-explanatory together with the given comments. The data set splitting in ``mode 1`` and the initial weights in ``mode 2`` rely on random numbers. For the reproducibility, a random number seed (keyword ``random_seed``) has to be defined, which will give the same results, if the run is repeated later. Together with this, the generator for the random numbers can also be defined (keyword ``random_number_type``). The second group of keywords describe the architecture of the NN and the activation functions of the nodes via the keywords ``global_...``. Usually, we use 2-3 hidden layers with 10-40 nodes each."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "### general keywords\n",
+ "nn_type_short 1 # 1=Behler-Parrinello energy is a sum of atomic energies\n",
+ "runner_mode 1 # 1=calculate symmetry functions, 2=fitting mode, 3=predicition mode\n",
+ "number_of_elements 1 # number of elements\n",
+ "elements Cu # specification of elements\n",
+ "random_seed 20 # integer seed for random number generator\n",
+ "random_number_type 6 # 6 recommended\n",
+ "\n",
+ "\n",
+ "### NN structure of the short-range NN\n",
+ "use_short_nn # use NN for short range interactions\n",
+ "global_hidden_layers_short 2 # number of hidden layers\n",
+ "global_nodes_short 15 15 # number of nodes in hidden layers\n",
+ "global_activation_short t t l # activation functions (t = hyperbolic tangent, l = linear)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Pyiron RuNNer Fit\n",
+ "\n",
+ "Here, you will not have the opportunity to explicitly change the ``input.nn``, since **RuNNer** is called via the pyiron environment. At the moment, the implementation of **RuNNer** to pyiron is on a very early stage, thus no changes are possible for the input."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pr = Project(\"runner_fit\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
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+ " vertical-align: middle;\n",
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+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>id</th>\n",
+ " <th>status</th>\n",
+ " <th>chemicalformula</th>\n",
+ " <th>job</th>\n",
+ " <th>subjob</th>\n",
+ " <th>projectpath</th>\n",
+ " <th>project</th>\n",
+ " <th>timestart</th>\n",
+ " <th>timestop</th>\n",
+ " <th>totalcputime</th>\n",
+ " <th>computer</th>\n",
+ " <th>hamilton</th>\n",
+ " <th>hamversion</th>\n",
+ " <th>parentid</th>\n",
+ " <th>masterid</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>finished</td>\n",
+ " <td>None</td>\n",
+ " <td>df1_A1_A2_A3_EV_elast_phon</td>\n",
+ " <td>/df1_A1_A2_A3_EV_elast_phon</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:53.061360</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>zora@cmti001#1</td>\n",
+ " <td>TrainingContainer</td>\n",
+ " <td>0.4</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>finished</td>\n",
+ " <td>None</td>\n",
+ " <td>df3_10k</td>\n",
+ " <td>/df3_10k</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:55.496691</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>zora@cmti001#1</td>\n",
+ " <td>TrainingContainer</td>\n",
+ " <td>0.4</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>finished</td>\n",
+ " <td>None</td>\n",
+ " <td>df2_1k</td>\n",
+ " <td>/df2_1k</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:56.101883</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>zora@cmti001#1</td>\n",
+ " <td>TrainingContainer</td>\n",
+ " <td>0.4</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>finished</td>\n",
+ " <td>None</td>\n",
+ " <td>df4_2_5eV_25A3_8K</td>\n",
+ " <td>/df4_2_5eV_25A3_8K</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:57.547918</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>zora@cmti001#1</td>\n",
+ " <td>TrainingContainer</td>\n",
+ " <td>0.4</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " id status chemicalformula job \\\n",
+ "0 1 finished None df1_A1_A2_A3_EV_elast_phon \n",
+ "1 2 finished None df3_10k \n",
+ "2 3 finished None df2_1k \n",
+ "3 4 finished None df4_2_5eV_25A3_8K \n",
+ "\n",
+ " subjob projectpath project \\\n",
+ "0 /df1_A1_A2_A3_EV_elast_phon /home/pyiron/ datasets/Cu_database/ \n",
+ "1 /df3_10k /home/pyiron/ datasets/Cu_database/ \n",
+ "2 /df2_1k /home/pyiron/ datasets/Cu_database/ \n",
+ "3 /df4_2_5eV_25A3_8K /home/pyiron/ datasets/Cu_database/ \n",
+ "\n",
+ " timestart timestop totalcputime computer \\\n",
+ "0 2021-02-18 19:49:53.061360 None None zora@cmti001#1 \n",
+ "1 2021-02-18 19:49:55.496691 None None zora@cmti001#1 \n",
+ "2 2021-02-18 19:49:56.101883 None None zora@cmti001#1 \n",
+ "3 2021-02-18 19:49:57.547918 None None zora@cmti001#1 \n",
+ "\n",
+ " hamilton hamversion parentid masterid \n",
+ "0 TrainingContainer 0.4 None None \n",
+ "1 TrainingContainer 0.4 None None \n",
+ "2 TrainingContainer 0.4 None None \n",
+ "3 TrainingContainer 0.4 None None "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_pr.job_table()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "j = pr.create.job.RunnerFit('fit', delete_existing_job=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Be aware of fitting a larger data set, since it will run some time, roughly six hours!\n",
+ "j.add_job_to_fitting(data_pr.load('df1_A1_A2_A3_EV_elast_phon'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job fit was saved and received the ID: 68\n"
+ ]
+ }
+ ],
+ "source": [
+ "j.run()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
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+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Name</th>\n",
+ " <th>Filename</th>\n",
+ " <th>Model</th>\n",
+ " <th>Species</th>\n",
+ " <th>Config</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>RuNNer-Cu</td>\n",
+ " <td>[/home/pyiron/day_2/02-runner/runner_fit/fit_hdf5/fit/input.nn, /home/pyiron/day_2/02-runner/runner_fit/fit_hdf5/fit/weights.029.data, /home/pyiron/day_2/02-runner/runner_fit/fit_hdf5/fit/scaling....</td>\n",
+ " <td>RuNNer</td>\n",
+ " <td>[Cu]</td>\n",
+ " <td>[pair_style nnp dir \"./\" showew no showewsum 0 resetew no maxew 100 cflength 1.8897261328 cfenergy 0.0367493254 emap \"1:Cu\"\\n, pair_coeff * * 12\\n]</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Name \\\n",
+ "0 RuNNer-Cu \n",
+ "\n",
+ " Filename \\\n",
+ "0 [/home/pyiron/day_2/02-runner/runner_fit/fit_hdf5/fit/input.nn, /home/pyiron/day_2/02-runner/runner_fit/fit_hdf5/fit/weights.029.data, /home/pyiron/day_2/02-runner/runner_fit/fit_hdf5/fit/scaling.... \n",
+ "\n",
+ " Model Species \\\n",
+ "0 RuNNer [Cu] \n",
+ "\n",
+ " Config \n",
+ "0 [pair_style nnp dir \"./\" showew no showewsum 0 resetew no maxew 100 cflength 1.8897261328 cfenergy 0.0367493254 emap \"1:Cu\"\\n, pair_coeff * * 12\\n] "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "j.lammps_potential"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The data set, you will fit is a strongly reduced subset of the data shown above. For comparison, the same plot is shown here.\n",
+ "\n",
+ "**Be aware of fitting a larger data set, since it will run some time, roughly six hours!**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of points in plot: 105\n"
+ ]
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 3686.4x2073.6 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df1_job = data_pr.load('df1_A1_A2_A3_EV_elast_phon')\n",
+ "df1 = df1_job.to_pandas()\n",
+ "fig1 = fc.PlotData(df1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## RuNNer Mode 1\n",
+ "\n",
+ "In **RuNNer**'s mode 1 the following steps are performed:\n",
+ "- calculation of SF values,\n",
+ "- splitting of data set in train and test data set.\n",
+ "\n",
+ "The amount of test structures is defined by the keyword ``test_fraction``. Here, ``test_fraction 0.10`` means 10% of the data set will be used for testing and is not part of the training data. ``use_short_forces`` keyword states to use also the atomic forces for the fitting process in ``mode 2``, but it is recommended to use it also in ``mode 1`` to create the necessary force files."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "### symmetry function generation ( mode 1):\n",
+ "test_fraction 0.10000 # threshold for splitting between fitting and test set\n",
+ "use_short_forces # use forces and prepare the files in mode 1 for fitting in mode 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the next group of keywords, the SFs are defined. There are two different types of SFs: the radial SFs ``symfunction_short XX type XX ...`` to describe the atomic distances and angular SFs ``symfunction_short XX type XX XX ...`` to describe the spatial distribution of the neighboring atoms. ``cutoff_type`` keyword describes the cutoff function type. All mentioned SF types are shown in the next section."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "### symmetry function definitions (all modes):\n",
+ "cutoff_type 1\n",
+ "symfunction_short Cu 2 Cu 0.000000 0.000000 12.000000\n",
+ "symfunction_short Cu 2 Cu 0.006000 0.000000 12.000000\n",
+ "symfunction_short Cu 2 Cu 0.016000 0.000000 12.000000\n",
+ "symfunction_short Cu 2 Cu 0.040000 0.000000 12.000000\n",
+ "symfunction_short Cu 2 Cu 0.109000 0.000000 12.000000\n",
+ "\n",
+ "symfunction_short Cu 3 Cu Cu 0.00000 1.000000 1.000000 12.000000\n",
+ "symfunction_short Cu 3 Cu Cu 0.00000 1.000000 2.000000 12.000000\n",
+ "symfunction_short Cu 3 Cu Cu 0.00000 1.000000 4.000000 12.000000\n",
+ "symfunction_short Cu 3 Cu Cu 0.00000 1.000000 16.000000 12.000000\n",
+ "symfunction_short Cu 3 Cu Cu 0.00000 -1.000000 1.000000 12.000000\n",
+ "symfunction_short Cu 3 Cu Cu 0.00000 -1.000000 2.000000 12.000000\n",
+ "symfunction_short Cu 3 Cu Cu 0.00000 -1.000000 4.000000 12.000000\n",
+ "symfunction_short Cu 3 Cu Cu 0.00000 -1.000000 16.000000 12.000000"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Definition of the Symmetry Functions (SFs)\n",
+ "\n",
+ "Different types of SFs for the radial and angular SFs are implemented in **RuNNer**, but only the most common types are shown here. SFs provide the input for the NN and describe the local atomic environment of each atom and are rotationally and translationally invariant. So, SFs describe the relative positions of the atoms to each other. In contrast, Cartesian coordinates describe the absolute positions to each other and change with a translation or a rotation. That means the numerical input will change with translation or rotation, but not the energy of the system. However, different numerical inputs belonging to the same energy leads to problems in fitting."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The Cutoff Function\n",
+ "\n",
+ "Another kind of symmetry function is the cutoff function, which is included in the radial and angular SFs. The cutoff radius $R_\\mathrm{c}$ (usually $12\\,\\mathrm{bohr}$) defines how much of the local atomic environment is considered. All SFs will decrease to zero, if the atomic distance is larger than $R_\\mathrm{c}$. A decrease to exact zero is necessary for numerical reasons. There are several cutoff funtions defined in **RuNNer** and we will use here\n",
+ "\n",
+ "\\begin{equation}\n",
+ " f_{c}(R_{ij}) = \n",
+ " \\begin{cases}\n",
+ " 1& ~ \\text{for $R_{ij} \\leq R_{inner,c}$}\\\\\n",
+ " 0.5 * [cos(\\pi x) + 1]& ~ \\text{for $R_{inner,c} \\leq R_{ij} \\leq R_\\mathrm{c}$},\\\\\n",
+ " 0& ~ \\text{for $R_\\mathrm{c} < R_{ij}$}\n",
+ " \\end{cases}\n",
+ "\\end{equation}\n",
+ "\n",
+ "with the atomic distance $R_{ij}$, the cutoff radius $R_\\mathrm{c}$, the inner cutoff $R_{inner,c}$ (here $=0$) and $x = \\frac{R_{ij} - R_{inner,c}}{R_\\mathrm{c} - R_{inner,c}}$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "distances = np.arange(0,15.1,0.1)\n",
+ "cfct = np.array([fc.cutofffct(i) for i in distances])\n",
+ "plt.plot(distances, cfct);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The Radial Symmetry Functions\n",
+ "\n",
+ "To define the parameters for the radial SFs, it is important to know which are the shortest bonds in your data set. Usually, 5-6 radial SF are used for each element pair, with different $\\eta$ values to increase the resolution for structure description. It is possible to shift the maximum of the radial SF $G^2$ by $R_{s}$\n",
+ "\n",
+ "\\begin{equation}\n",
+ " G_{i}^{2} = \\sum_{j}^{}e^{\\eta (R_{ij} - R_{s})^2} \\cdot f_{c}(R_{ij}).\n",
+ "\\end{equation}\n",
+ "\n",
+ "Below, the defintion of a radial SF in ``input.nn``, again ``symfunction_short`` calls to define a SF, ``Cu`` defines the specific element, ``2`` the SF type, the second ``Cu`` defines the neighboring atom, and the last three parameters define $\\eta$, $R_{s}$ and $R_\\mathrm{c}$. The gaussian exponent $\\eta$ for the radial SF are chosen to equally distribute the radial SF turning points, whereas the turning point of radial SF with $\\eta = 0$ is set to the specific minimum bond in your data set. There is no need to define element specific SF, also global SF are possible, which are used for every element combination. It is also possible to define for each SF a different $R_\\mathrm{c}$, but it is recommended to use only one $R_\\mathrm{c}$ for all SFs. "
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "symfunction_short Cu 2 Cu 0.000000 0.000000 12.000000"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, different radial parts of radial SFs with different $\\eta$ are plotted. Feel free and play around with the parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rsf1 = np.array([fc.radialSF(i, 0.1) for i in distances])\n",
+ "rsf2 = np.array([fc.radialSF(i, 0.05) for i in distances])\n",
+ "rsf3 = np.array([fc.radialSF(i, 0.025) for i in distances])\n",
+ "plt.plot(distances, rsf1[:,1], label='');\n",
+ "plt.plot(distances, rsf2[:,1], label='');\n",
+ "plt.plot(distances, rsf3[:,1], label='');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The Angular Symmetry Functions\n",
+ "\n",
+ "For the angular SF it is quite similar as for the radial SF. But here, three atomic positions are included.\n",
+ "\n",
+ "\\begin{equation}\n",
+ " G_{i}^{3} = 2^{\\zeta - 1}\\sum_{j}^{} \\sum_{k}^{} \\left[( 1 + \\lambda \\cdot cos \\theta_{ijk})^{\\zeta} \\cdot e^{\\eta (R_{ij}^2 + R_{ik}^2 + R_{jk}^2)} \\cdot f_{c}(R_{ij}) \\cdot f_{c}(R_{ik}) \\cdot f_{c}(R_{jk}) \\right],\n",
+ "\\end{equation}\n",
+ "\n",
+ "the angle $\\theta_{ijk} = \\frac{\\mathbf{R}_{ij} \\cdot \\mathbf{R}_{ik}}{R_{ij} \\cdot R_{ik}}$ is centered at atom $i$ and the atomic distance vector is defined as $\\mathbf{R}_{ij} = \\mathbf{R}_{i} - \\mathbf{R}_{j}$. Mostly used for the angular exponent $\\zeta = 1, 2, 4 ,16$, gaussian exponent $\\eta = 0$ and for $\\lambda$ only $+1$ or $-1$ is possible. If many atoms of each element are present, angular SFs are usually not critical and a default set of SFs can be used.\n",
+ "\n",
+ "Here a definition of an angular SF is given, which is similar to the definition of a radial SF. ``3`` defines the used type of SF, which needs the following parameters: ``Cu Cu`` to describe the neighboring atoms included in the angle, followed by $\\eta$, $\\lambda$, $\\zeta$ and $R_\\mathrm{c}$."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "symfunction_short Cu 3 Cu Cu 0.00000 1.000000 1.000000 12.000000"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Below, you find different angular parts for angular SF with different $\\zeta$ and $\\lambda$ values. Fell free and play around."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "angles = range(0,361)\n",
+ "asf1 = np.array([fc.angularSF(i,1,1,1,0.0,1.0,1.0) for i in angles])\n",
+ "asf2 = np.array([fc.angularSF(i,1,1,1,0.0,1.0,2.0) for i in angles])\n",
+ "asf3 = np.array([fc.angularSF(i,1,1,1,0.0,1.0,4.0) for i in angles])\n",
+ "asf4 = np.array([fc.angularSF(i,1,1,1,0.0,-1.0,1.0) for i in angles])\n",
+ "asf5 = np.array([fc.angularSF(i,1,1,1,0.0,-1.0,2.0) for i in angles])\n",
+ "asf6 = np.array([fc.angularSF(i,1,1,1,0.0,-1.0,4.0) for i in angles])\n",
+ "plt.plot(angles, asf1[:,1]);\n",
+ "plt.plot(angles, asf2[:,1]);\n",
+ "plt.plot(angles, asf3[:,1]);\n",
+ "plt.plot(angles, asf4[:,1]);\n",
+ "plt.plot(angles, asf5[:,1]);\n",
+ "plt.plot(angles, asf6[:,1]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Output Mode 1\n",
+ "\n",
+ "As said, the data set splitting and the calculation of the symmetry function values takes place here. Among general information of your data set and your SFs, you find explicitly how the data set is splitted. As shown in the example below, it is written what happens to each structure (called ``Point``) and if it goes to the training or test set and which number it has there. ``mode 1`` will prepare the necessary files for ``mode 2``:\n",
+ "* training data\n",
+ " - function.data: SF values for each atom in each structure\n",
+ " - trainstruct.data: structural information\n",
+ " - trainforces.data: force information (if force fitting is used)\n",
+ " \n",
+ "\n",
+ "* test data\n",
+ " - testing.data: SF values for each atom in each structure\n",
+ " - teststruct.data: structural information\n",
+ " - testforces.data: force information (if force fitting is used)"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ " -------------------------------------------------------------\n",
+ " Maximum number of atoms: 128\n",
+ " -------------------------------------------------------------\n",
+ " Calculating Symmetry Functions\n",
+ " for 8073 structures\n",
+ " -------------------------------------------------------------\n",
+ " 1 Point is used for training 1\n",
+ " 2 Point is used for training 2\n",
+ " 3 Point is used for training 3\n",
+ " 4 Point is used for testing 1\n",
+ " 5 Point is used for training 4\n",
+ " 6 Point is used for training 5"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**RuNNer** ends without any problems, if at the end of the ouput file the following lines are written:"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ " Normal termination of RuNNer\n",
+ " -------------------------------------------------------------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Short quick example of SF calculation\n",
+ "\n",
+ "To clarify how the SFs represent the atomic environment for an atom. Let's have a look at this simple structure with three Cu atoms below:"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "begin\n",
+ "atom 0.00000000 0.00000000 0.00000000 Cu 0.00000000 0.00000000 -0.00000000 -0.00000000 0.00000002\n",
+ "atom 0.00000000 0.00000000 6.72752138 Cu 0.00000000 0.00000000 0.00000000 0.00000000 -0.00000004\n",
+ "atom 2.34735543 -1.35524733 3.36375974 Cu 0.00000000 0.00000000 0.00000000 -0.00000134 0.00000003\n",
+ "energy -0.4746414926841609\n",
+ "charge 0.0\n",
+ "end"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we define the atomic positions as vectors, calculate the distances and angles for the SFs. Finally, we will end up with the SFs vector for the first Cu atom. We use the 13 SFs, which were introduced in the section above \"RuNNer Mode 1\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.1182423115740479\n",
+ "0.9463318326255568\n",
+ "0.7253600030997174\n",
+ "0.40425285668735783\n",
+ "0.09616369636385737\n",
+ "0.411992195819298\n",
+ "0.4119731729121053\n",
+ "0.41193512973271224\n",
+ "0.41170694441910877\n",
+ "1.9023785577599554e-05\n",
+ "8.78384860610243e-10\n",
+ "1.872667361071411e-18\n",
+ "1.7583909381106493e-70\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Define atomic positions as vectors\n",
+ "d1 = np.array([0.0, 0.0, 0.0])\n",
+ "d2 = np.array([0.0, 0.0, 6.72752138])\n",
+ "d3 = np.array([2.34735543, 1.35524733, 3.36375974])\n",
+ "\n",
+ "# Define distance vectors\n",
+ "d12 = d1 - d2\n",
+ "d13 = d1 - d3\n",
+ "d23 = d2 - d3\n",
+ "\n",
+ "# Define angles\n",
+ "a123 = np.dot(d12, d13) / (np.linalg.norm(d12) * np.linalg.norm(d13))\n",
+ "a213 = np.dot(d12, d23) / (np.linalg.norm(d12) * np.linalg.norm(d23))\n",
+ "a312 = np.dot(d13, d23) / (np.linalg.norm(d13) * np.linalg.norm(d23))\n",
+ "\n",
+ "# Calculate radial symmetry function values\n",
+ "for eta in [0.000, 0.006, 0.016, 0.040, 0.109]:\n",
+ " value_sf = 0\n",
+ " for d in [d12, d13]:\n",
+ " d = np.linalg.norm(d)\n",
+ " value_sf += fc.radialSF(d, eta)[0]\n",
+ " print(value_sf)\n",
+ "\n",
+ "# Calculate angular symmetry function values\n",
+ "for Lambda in [1, -1]:\n",
+ " for zeta in [1, 2, 4, 16]:\n",
+ " for a in [a123]:\n",
+ " value_sf = fc.angularSF(a, np.linalg.norm(d12), np.linalg.norm(d13), np.linalg.norm(d23), 0.0, Lambda, zeta)[0]\n",
+ " print(value_sf)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## RuNNer Mode 2\n",
+ "\n",
+ "In ``mode 2``, the magic happens and your data will be fitted. The part below of the ``input.nn`` defines how the fitting in ``mode 2`` has to take place. ``epochs`` define how often **RuNNer** will loop over the training data to optimize the weights and biases of the NN, ``fitting_unit`` defines in which unit the output will be presented in ``mode 2``, all other files and units will stay in ``bohr`` and ``Hartree``. ``precondition_weights`` effects the initial weights and biases of the NN. In the second part, there are some parameters for the Kalman-Filter, ``repeated_energy_update`` repeats the energy update after a force component update, to increase the impact of the energies. This is slower in general, but might be necessary, since there a many more force components than energies. ``mix_all_points`` mixes the order of the training points for each epoch to improve the training. Often, the ranges of the symmetry functions are rather different in their order of magnitude and thus a rescaling of SFs can be advantageous numerically stated by ``scale_symmetry_functions`` keyword. Together with that, a centering of the SF average value to zero is performed for numerical reasons, since zero is the non-linear center of most activations functions. ``short_force_fraction`` defines how much of the force components is randomly used for training the NN. The last part, defines to write certain files for each epoch, to analyze it in a later stage. There are many other keywords and options to present. However, you got an idea how **RuNNer** works and what to do to fit your first NNP. In the next part, first steps for analyzing the fit are presented."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "### fitting (mode 2):general inputs for short range AND electrostatic part:\n",
+ "\n",
+ "epochs 10 # number of epochs\n",
+ "fitting_unit eV # unit for error output in mode 2 (eV or Ha)\n",
+ "precondition_weights # optional precondition initial weights\n",
+ "\n",
+ "\n",
+ "### fitting options ( mode 2): short range part only:\n",
+ "\n",
+ "short_energy_error_threshold 0.10000 # threshold of adaptive Kalman filter short E\n",
+ "short_force_error_threshold 1.00000 # threshold of adaptive Kalman filter short F\n",
+ "kalman_lambda_short 0.98000 # Kalman parameter short E/F, do not change\n",
+ "kalman_nue_short 0.99870 # Kalman parameter short E/F, do not change\n",
+ "use_short_forces # use forces for fitting\n",
+ "repeated_energy_update # optional: repeat energy update for each force update\n",
+ "mix_all_points # do not change\n",
+ "scale_symmetry_functions # optional\n",
+ "center_symmetry_functions # optional\n",
+ "short_force_fraction 0.01 #\n",
+ "\n",
+ "\n",
+ "### output options for mode 2 (fitting):\n",
+ "write_trainpoints # write trainpoints.out and testpoints.out files\n",
+ "write_trainforces"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "During the fitting process of the NN, the error function $\\Gamma$ is minimized, which is defined as \n",
+ "\\begin{equation}\n",
+ " \\Gamma = \\frac{1}{N_\\mathrm{struct}} \\sum_{i}^{N_\\mathrm{struct}} (E_{NN}^{i} - E_{Ref}^{i})^2 = RMSE(E),\n",
+ "\\end{equation}\n",
+ "if only energy fitting is used, which defines simultaneously the root-mean squared error of the energies $RMSE(E)$. This defines the differences of the reference data and the NNP predictions. During the epochs, the error decreases as you can see in the part of ``mode2`` output."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ " -------------------------------------------------------------------------------\n",
+ " RMSEs (energies: eV/atom, forces: eV/Bohr):\n",
+ " --- E_short: --- - time -\n",
+ " /atom min\n",
+ " epoch train test\n",
+ " ENERGY 0 0.486020 0.481254 9.86\n",
+ " FORCES 0 0.543702 0.502894\n",
+ " -------------------------------------------------------------------------------\n",
+ " ENERGY 1 0.039459 0.039840 19.05\n",
+ " FORCES 1 0.201312 0.174885\n",
+ " INFORMATION USED FOR UPDATE (E,F) 1 1998 45\n",
+ " -------------------------------------------------------------------------------\n",
+ " ENERGY 2 0.024635 0.026306 19.14\n",
+ " FORCES 2 0.132738 0.123616\n",
+ " INFORMATION USED FOR UPDATE (E,F) 2 5565 112\n",
+ " -------------------------------------------------------------------------------\n",
+ " ENERGY 3 0.022316 0.024581 19.13\n",
+ " FORCES 3 0.120274 0.111427\n",
+ " INFORMATION USED FOR UPDATE (E,F) 3 6033 131\n",
+ " -------------------------------------------------------------------------------\n",
+ " ENERGY 4 0.021333 0.023145 19.16\n",
+ " FORCES 4 0.113496 0.105447\n",
+ " INFORMATION USED FOR UPDATE (E,F) 4 6132 142\n",
+ " -------------------------------------------------------------------------------\n",
+ " ENERGY 5 0.022327 0.023597 19.13\n",
+ " FORCES 5 0.113152 0.102596\n",
+ " INFORMATION USED FOR UPDATE (E,F) 5 6064 137\n",
+ "-------------------------------------------------------------------------------\n",
+ " ENERGY 6 0.021007 0.022555 19.15\n",
+ " FORCES 6 0.102685 0.094464\n",
+ " INFORMATION USED FOR UPDATE (E,F) 6 6094 168\n",
+ " -------------------------------------------------------------------------------\n",
+ " ENERGY 7 0.021018 0.022213 19.15\n",
+ " FORCES 7 0.098023 0.097181\n",
+ " INFORMATION USED FOR UPDATE (E,F) 7 6226 158\n",
+ " -------------------------------------------------------------------------------\n",
+ " ENERGY 8 0.020692 0.022248 19.15\n",
+ " FORCES 8 0.095995 0.097202\n",
+ " INFORMATION USED FOR UPDATE (E,F) 8 6186 183\n",
+ " -------------------------------------------------------------------------------\n",
+ " ENERGY 9 0.020880 0.022219 19.16\n",
+ " FORCES 9 0.094960 0.095833\n",
+ " INFORMATION USED FOR UPDATE (E,F) 9 6122 176\n",
+ " -------------------------------------------------------------------------------\n",
+ " ENERGY 10 0.021217 0.022457 19.41\n",
+ " FORCES 10 0.097554 0.094895\n",
+ " INFORMATION USED FOR UPDATE (E,F) 10 6226 203\n",
+ " =============================================================\n",
+ " Best short range fit has been obtained in epoch 7\n",
+ " --- E_short: --- --- F_short: ---\n",
+ " train test train test\n",
+ " OPTSHORT 0.021018 0.022213 0.098023 0.097181\n",
+ " -------------------------------------------------------------\n",
+ " max Eshort error in last epoch (train set): 0.281291 eV/atom (structure 788 )\n",
+ " max Eshort error in last epoch (test set) : 0.261851 eV/atom (structure 253 )\n",
+ " -------------------------------------------------------------\n",
+ " Total runtime (s) : 12095.013\n",
+ " Total runtime (min): 201.584\n",
+ " Total runtime (h) : 3.360\n",
+ " Normal termination of RuNNer\n",
+ " -------------------------------------------------------------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A first and simple plot to anlyze the progress of the fitting procedure, is to show the RMSEs over the epochs. Here, you can easily identify overfitting, if the training $RMSE$ is much lower than the test $RMSE$, for example.\n",
+ "Anyhow, the $RMSE$ is a rather strong reduction of the really complex potential energy surface (PES) and can only be understood as a rule of thumb for the quality of the NNP fit."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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5jBw5kpiYGIDsdN/r1q07Y8nRv/71r9x4442F1sE33fedd97JyJEj2bZtG126dEFVqVevHh988EGJX2uh6b5FZD6QAHwPnPLer6rX5veYcCl2uu/8DBnirhwmTXLJ/MaOhZEjg/f8xphCWbrv4Clquu9AriCeCEK9yqY33nA5mlRtJJMxpsIpNECo6te+ZRG5FLgV+Nr/I8qR2Fj3Mz3d9UNYgDDGVCABZXMVkQQR+Y8n/fY/KY1lQCPFggWu36FhQzfUNUQr8Blj8heqlS8rkuK8h/kGCBE5V0QeF5G1wIu4ld9EVXur6ovFr2YZEx8PV1wBbdvaSCZjwiAmJoaDBw9akCgBVeXgwYPZHeWBKqiJaR0urcY1qroJQERGF7+KZVSdOjBtGsyfD//6l41kMqaUNW3alOTkZIq9pLABXKD1TfkRiIICxA24xXrmi8hc3MpuFXeWmHc50zVrIM8sSmNM6ERHR5d4RrApnnybmFR1pqreApyPW49hNNBARMaLSJ9Sql/kuOMOiIqyjmpjTIURSLK+k6o6WVUH4taMXgE8HOqKRZznn4fOnS1AGGMqjII6qZeJyPMi0k9EYgBU9ZCqTlTVy0uvihGiZ0+46CIbyWSMqTAKuoK4GJgJJAJfi8gcERkpIueWSs0iUYMGbiTTxo3hrokxxoRcQX0QGar6lao+rKrdgbuB48A/ReRHEXmp1GoZKbzpdoOQ48QYYyJdQU1MN3qblgBUdbeqvq6qN+PWh5hcGhWMKN4kWlWqhLcexhhTCgpqYvo1sF1EJolIfxGJ8u5Q1SxV/Tb01YswDRq4q4jVqyErK9y1McaYkCqoiel6oC3wBfAAsMMzxLVXaVUuIrVvDzNmwD33hLsmxhgTUgUOc1XVY6r6pqr2By7ADXH9n4jsKI3KRaQOHeDECWjRItw1McaYkAo0WV8tYDBwC1AbeD+UlYpo8fFw6hTcdVe4a2KMMSFVUCd1dRG5XUTm4LK3XoTL5NpcVUeVUv0iT1yc+7lqFSxfbnMijDHlVkFXEFuBfsB4oJmqjlDVL7Wip1T0Lik4eTJ07QqLF4e3PsYYEyIFJetrrqopACISKyKtVXV9KdUrctWv70YyVaoEEyfCBReEu0bGGBMSBY1i8gaHa3Cd03M95QQR+ahUahep4uNhwwYYMQKqVw93bYwxJiQC6aR+AugGHAFQ1RVAy1BVqEyIi8uZCzFtGsycGe4aGWNM0AUSIDJU9WjIa1KWxMfD0aOwZ4/L8vr66+GukTHGBF0gAWKViNwKRIlIOxH5H7AokCf3ZIJdLyKbROSMFOHivODZv1JEunjujxGR70XkJxFZLSJPFulVhZq3o3r1aruCMMaUW4EEiPuBeOAU8A5wFBhV2IM8qTnGAf2BOGCoiMTlOaw/0M6zjcCNmMJzrstVtROQAPQTkYsDqGvp8A51Xb0amjWDypUhLQ327g1vvYwxJogKGsUEZHdWP+rZiqIbsElVtwCIyFRgELDG55hBwCTP0NklIlJTRBqp6m7ghOeYaM8WOcNrvSOZ1nheSlYWXHaZu2/u3PDWzRhjgqTQAFECTQDflBzJQPcAjmkC7PZcgSzH5YMap6rf+TuJiIzAXX3QvHnz4NQ8EPHxOavLVaoEo0e7wGGMMeVEQKk2ikn83Jf3KiDfY1Q1U1UTcMucdhORDv5Ooqovq2pXVe1ar169ktS3aLwjmbzzBn/9a7jqqtI7vzHGhFgoA0Qy0Myn3BTYVdRjVPUI8BVuVnfk8I5k2r075z5VeOYZ+Pe/w1cvY4wJknybmDyjlfJt91fVBwp57qVAOxFpBewEhgC35jnmI+A+T/9Ed+Coqu4WkXpAuqoeEZFY4Erg6UJfTWnyHcnUuLG7LQI//QQnT7pgIf4ukIwxpmwoqA9imefnpbhRSNM85ZtwfQMFUtUMEbkP+BSIAl5X1dUico9n/wRgDjAA2ASkAN4UqY2ANz39EJWAd1X146K8sJDzDRC+TUuvvGIrzhljyoV8A4SqvgkgIncCvVU13VOeAMwL5MlVdQ4uCPjeN8HntgL3+nncSqBzIOcIm3r1claX8+UNDvv3wy+/uIR+xhhTBgUyiqkxUB045Cmf7bnPxMfnDHXN68YbYdcuWLcOoqL8H2OMMREskADxFPCjiMz3lH+Fy89k4uNd2m9//Q3PPQexsRYcjDFlViAT5d4QkU/ImcPwsKruCW21yoi4ODeSadcuaNIk974uXXJuZ2ZaoDDGlDmBDnONAvYDh4FzRaRX6KpUhng7qvNrZgJ45BG47jpbec4YU+YUegUhIk/j1qJeDWR57lZgQQjrVTbkN5LJV+PGbthrRgZER5de3YwxpoQC6YO4DjhPVU+FuC5lT34jmXzdf3/p1ccYY4IokCamLbhkecafiy6Ct96CRx+FEyfyP27NGpg0qfTqZYwxJRRIgEgBVojIRM/aDS+IyAuhrliZ8dprbkjrv/4F554Lb77psrvm9dRT8NBDkJpa+nU0xphiCCRAfAT8A7dI0HKfzQA0agRvvw2LF7u1Ie68Ey6+2JV9PfMMrFzphr4aY0wZIFqORtd07dpVly1bVviBoZKV5YLFww+7JH633uquHJr55CNUdbOsLTW4MSYCiMhyVfWb8qHQKwjPMqPTRWSNiGzxbsGvZjlQqRLccQds2OD6JN5/H847D558ElJS3DF/+AN065ZTNsaYCBVIE9MbuKVAM4DewCTgrVBWqsw7+2z45z9dmo2BA+GJJ+D882HKFLj+enjgARvyaoyJeIEEiFhV/QLXHPWLqj4BXB7aapUTLVvCu+/C11+74bC33uomzvXqZQHCGFNyjzwCDz4YsqcPJECkiUglYKOI3Cci1wPWgF4UvXrB0qXw6quwaZMbGtu3LwwfbjOsjQmGrCz49tuc8r33uqbd8uaHH9yAF6/jx+HYsZCdLpAAMQqoCjwAXAjcBgwLWY3Kq6gouPtu2LgR/vQn+PxzSEqCv/wFDh8Od+2MKduefRZ69nTzjVTdl6bvvKSnn3ajCMuarCw3IjIz05XnzoXHHoNDnuTaL77o1qAJFVUtN9uFF16oZcaaNaoDB6qCarVqqvfdp7pxY7hrZUzZsWWL6oYN7vahQ6rvvKOalXXmcbt2qZ51lurYsa586pTq99/7PzYSZGWppqe72zNmuO+I+fNd+fBh1WPHgno6YJnm850ayjWpTUHat4dZs2DZMujYESZMcBPtrrvO9VlY05Mx+UtPd1cMo0a5cq1aMHSo/2V+GzWCffvgLs+ClZ995kYSfvaZK2dkRM7f29690Lo1vPGGK/fp4zIweLND16wJ1auXWnUsQITb7t3uEnLiRDc0duFCSEx0K9G9/TacPh3uGhoTGbKyYPZs92UeHe2aaCdODOyxNWrAOee425deCq+/7v7OAMaNc8PRjxxx5S1b3NryXvPmuSHrXi++CP/5T075vvvc5nXDDTDMpxW+b183gdbr0ktzlzt1ygle9evDFVdAixauXK0a3H57Tt1LWb4BQkRsrczSMHCg61wbPhz+8Q/YscN96FNS3AejVSv4979z2hyNqajeecf9vcz3rF125ZXQtGnRn6dmTfeFfNZZrty6NfTu7e4HN9F16NCc48ePh7//Pae8YAF8+WVOuUqV3OvQd+yYk+kZ4LLLci89fPXV7urH69Zb3WsBdwX06qvuyiEC5DuTWkR+xC0vOgWYqqoFLHoQGcI+k7qktm51qcE7dHD/LX36qVuZ7rPPoGpV91/HyJGuKcqYUEpJcZ+5cNu61WUe6NbNNSt9/LFrhvXXlBQsy5e71+/9Et+3z02CrVs3dOcMo4JmUheYakNEzgOG4NaDOE1OsPglFBUtqTIdIFRdO6OI+4D6/gH8/LMLFJMnuz+SgQNh9Gh3iRzKPxRTMWzZ4kb/DBzoyr/5jfsvecMGV374YTecctw4V/a3xG4oqELnzu7LOe/fhAmaYqfaUNX1qvqkqsbhhrbWBL4UkW8LepwpBhHXLvruu2f+IVxwgdu3fbsb4rZ4MVx+uQsoL71kw2RN0Xz5pQsC3qzDL78Mgwe7fz7ABYp77snpuM3IcJtX795uv9e+fcXv5M3MdEv2erMcr1vnPuPp6Tl/E7NmWXAIk4A6qT0T5eoDDYBquOVHTbB17gxt27rb/q6EGjRwk3+2b3djn1XdhKBGjVyb6Wef+U81bsoWVde0cvSoK2/eDLfdltNxumED/Pa3sHatK69f79K3bN7symvXwp//7PqzAGbOdJ+d3btdecsW+OQT98UO7sv+hx9y1k2/7jqXM8z7pTxmTO7O4F693GfVW9f27V3Tp9fChe6KA9wX/8KFsMezjP2mTS49vvfz/fXXbj33JUtcedcu1wH8wQeu3KXLmeu9m9KT3/hXT9NTT+AlYBcwDxgO1CjoMeHcytQ8iIJ4xz7PmVPwcVlZqsuXuzkUtWq5xzRvrvrYY6qbN5dOXU3JpaaqTp/u5saoqq5c6X6Xkye78o8/qrZpo/rVV668eLFq48aqCxe68vz57ve/eLErz5mjGhOjumyZKy9bpjp8uOovv7hyZmbw6n76tOr48Tl127fP1f3pp115wwZXfvNNV960SfX881U//dSV9+51j9+xw5UzMiJ3fkI5RQHzIAoKDjuAb4H7gQb5HRdJW7kJEKdPq/7vf+5noFJTVadNU+3bV1XE/WoTE1UnTVI9eTJ0dTVFl56u+pe/uH8EVFWPH3e/s7/9LWf/+PGqW7eGq4bFl5qqOneum8Sm6ialffqpCwQmIhUUIAoaxdRCfTqjRaSaqp4M4cVMiZXpTur8nDgBaWlFG0GxY4ebXPPGG67ZoXp1GDLEDaXt3t3ac8NhyBA3nPJf/3Ll1q3hllvcEGZwaSDOPRdiYsJXR1MhFauT2hscRKSHiKwB1nrKnUTkpZDU1OSWleUmzdx8c9E6AZs1c5PuNm50bbyDB7sRUD16QFwc/Pe/OW3CpnTUrOnSwHtt3JgTHMCNnbfgYCJMoSvKich3wI3AR6ra2XPfKlXtUAr1K5JyeQXx3nsujYB3Ik1xHT/uRki9/josWuSGDjZt6joAGzd2P72bb7lateC8jopozhw3gMDboWtMBCr2PAjPg79T1e4i8qNPgPhJVTuFoK4lUi4DhK8jR3Jme5bE+vUwdaprftq50227duWMPPFVo4b/IBIf7yYv2Rrb/mVkuPeoefOcnD/GRKCCAkTlAB6/Q0QuAVREzsKl/V4bzAqaAMyd69qsvYnGSuK88+Bvfzvz/uPHc4KFN3D4lr/4wg2V9KYejo52KQR69nTbJZdA7dolq5s/qu68q1a5L97+/SO/H6VyZZfS3QKoKcMCuYKoCzwPXAkIbrjrSFU9WOiTi/TzPDYKeFVVn8qzXzz7BwApwJ2q+oOINMMtbdoQyAJeVtXnCztfub6COHQI/vhH139Qp0746pGZ6cbPL18O33zjxrgvXZozyapDB5d7xhs0mjUr2vMfOeICwapVbga597ZvLqrbbnPzQCKxzX7zZjeG33cegTERrERNTCU4aRSwAbgKSAaWAkPVJ6eTiAzADaMdAHQHnvc0ZzUCGnmCRXVgOXCdFpIPqlwHCF9ZWe6LOlKWLU1Nhe+/d8Him29cH4e3uap5cxcovEGjfXvX/5Ga6iZ05Q0Eyck5z3vOOS7geLcLLnDP//jj7mpl5kyX/TKSPPKIC16rVkHDhuGujTGFCleA6AE8oap9PeVHAFT13z7HTAS+UtUpnvJ6IFFVd+d5rg+BF1W1wMbcChEgMjPdTNT69QNPdZxXqHPpZGa6YZveK4xvvskZNVW7trsC2rw5Z9Z3lSoucPgGgg4d3NWHv3pOnw533OHeg1mz3PGRIivLzXRv2TLcNTEmICXtgyiuJrjJdl7JuKuEwo5pAmQHCBFpCXQGvvN3EhEZAYwAaN68eUnrHPmiotwXYp067ov+yBGXedKbjuCTT1xzz7XXuvJf/uLawR97zJUvucQd+957rvzxx25uRL16wa1j585ue+ABV8/Nm3OCxdGjLsWxNyC0beva7AN1443uC3jQIPd6pkzJSTQXDikprvnvySfd+2jBwZQToQwQ/v5FzXu5UuAxInI28D4wSlX9rsytqi8DL4O7giheVcsY39z0t94KBw+6Jh5wa/OePJkTILZvz91ReuONbtgsuC/qG26A3//eZYsF1wlclC/rQIi4INC2be6FUkqia1f3mgcNcq91zBiX4TYc7f4//eQmJg4YEN5AZUyQFfpNICJVgBuAlr7Hq+rf83uMRzLg20PZFJfTKaBjRCQaFxwmq+qMwupZYY0aBadO5ZTfeitnIRRwq9L5+sMfcm7XqOE6m72rVa1cCVdd5VbPuuyykFU5aJo0cWmp77gDHnzQ9WmMG5f79ZeGHj1ccr1gXoUZEwECyeb6ITAIyABO+myFWQq0E5FWnuGxQ4CP8hzzEXCHOBcDR1V1t2d002vAWlV9NsDXUjH17ZtztQCuY7QoQ007dHAdyeD+++7ZE84/35UXLHBfuGlpwatvsFWt6iYAPvqoW4mrb193RRVqqvDQQ/CR5yNtwcGUQ4G0JTRV1X5FfWJVzRCR+4BPccNcX1fV1SJyj2f/BGAObgTTJtwwV8/CrFwK3A78LCIrPPf9RVXnFLUepgguuMB1AHvNnOna90eMcOUdO9yEOW9a6EhRqRL8858usN19t+tT+fhjaNcup64TJ7ov8xkzci8PWVypqW7pS9/+HmPKmUDmQbwM/E9Vfy6dKhVfhRjFVNr27nVrCYDrdG7c2C0cD64PoFkzl04i3FJSXBNZVhZcfz0cO+b6Uo4ccUHi6afhxx/dDHJw6yXExOTuzynOOWNiXIAypowq9opyHpcBy0VkvYisFJGfRWRlcKtoIpY3OKi6kVD33ptTvuSSnGUoMzNd1thnnnHl06fdF/WsWa586pRb/H3NmpzyokVuvWFwneO7dwfenPXLL65D/sgRV05Kcn0BTZu6wNWwoeusf94zv/Khh3KCA7jzHjiQU3788dwL0efnvfdg2DB35VC1qgUHU64F8unuD7QD+gDXAAM9P01FIuKywg4Y4Mqq7kritttcOT3dNUV17OjKJ0+6oa3e5VAPHHCjpRYudOU9e+DSS11TELhO3saNXQc5uEASE+Oaubzl8893zTrgVlV78MGcVdYGDnQzmGvXhhYt3NXEgAHumPvvz71kJrhU6C95khKfOAETJuSsapaR4fozvCuu+dqyxb0u78xxYyJAZlZmSJ63oPUgzlHVYyLit8dTVQ/5uz+crIkpgmVluS/catXclUZKipsTER/v/us/fBimTXPpzdu1c/mfXnjBBaAOHdyX8qOPuqahLl3clcaxYwXPpM7MdMc/+yz06eOeP79kh5mZ7qqmalV3ZXPppe5q4cYb3VXKzp2uruCCQ6TMYjflQkZWBodSD3Ek7Uj2djTtaO7yqdxl3/tqVKlB8h+SCz+RH8WaSS0iH6vqQBHZipub4DvAXFW1dbFqE0IWIIxfr74K/+//uXkYf/87XHNNwXmcVGH1amjVygW0V191VyJLlrgZ38YU06mMU2w8tJE1+9fk2jYc3EB6Vv5XpVESRc2YmtSIqUHNmJo5WxV3X4NqDXjosoeKVadizaRW1YGen62KdVZjIsVvfuOCw+23u8WXatZ0mXGHDYOLLz5zcp2Iu2rxGjjQdXTb2hgmQCnpKaw/sD4nCBxwPzcf2kymuuYgQWhdqzVx9eIYeO5Amp7TlFoxtfwGgmrR1ZAwTAINWS6mcLArCFOgzEyXsnzSJDfcNTXVNWfdcYcLHi1ahLuGJsKdyjjFwdSDHEw5yIGUA9m3D6YeZP/J/dlXB9uObEM9SSGiJIp2ddoRVy+OuLpxxNWLo3299pxX5zxio8OfDj4syfrCwQKECdixY65D/M033bKsAImJ7qrihhtcP4mpEE5nnmbbkW1sOrSJLYe3sP/kfvfF7/PlfyDlAAdTDnIyPf85wlWjq9KmVhsXCHy2trXbclZUKc/uLwILEMYUZNs2l6Jk0iTYtMl1VA8e7IJF796RNzHQFFlqeipbDm9h06FNOdth93P70e1kaVau42vF1KJO1TrUia1Dnap1qFu1rrvtKZ9xf9U6xFSOwPVJAlDcTurLVfVLz+1WqrrVZ9/gSMyPZAHClIgqLF7sAsXUqS6ZYdOmbiTVsGE5KUhMRDqSdoSth7fmDgSeIJB8LPcIn9qxtWlbu63barXNvt26VmvqVq1LVKWK809BcQPED6raJe9tf+VIYQHCBE1ampvk9+abbrnXzEw3SfCFF2xyXJicyjjFtiPb2Hpka3Yg2Hpka3b5cNrhXMfXr1bfbxBoU7sNtWNDsDRuGVXc9SAkn9v+ysaULzExcNNNbtu7F/71LxccTp50w16t2SkkMrMyWbl3JSv3rsz+8t9yeAtbD29l1/Fd2R2/AGdFnUXLmi1pXas13Zt0p1XNVrSu1ZpWtVrRtnZbzqlyThhfSflQUIDQfG77KxtTfjVoAGPHukWa/vY3N/rprbdsslwQnM48zbJdy/jml29YsH0BC7cv5Ngpt/SLIDQ5pwmtarbiitZX0Lqm+/L3BoJG1RtRSexqLpQKChCtReQj3NWC9zaess2NMBWLiMvXFBvrZmefOuX6KYKRGbYCSUlPYUnyEhb8soAFvyxgSfISUjNSAWhftz1D4ofQq0UvujbuSsuaLalS2d7fcCooQAzyuT0mz768ZWMqhj/9yQWJ+++H665z8yliwz+WPVIdSTvCt9u/dQFh+wKW7VpGRlYGlaQSCQ0TGHHhCHq16MVlzS+jfrUC0qaYsChoJvXXvmXPCm8dgJ2q6ieLmTEVxH33uaDw29/C1Ve7dSbOPjvctcrlQMoBpq+Zzo+7f+SWDrfQu2XvUpuJ+8uRX3hp6Ut8uvlTVu5diaJEV4qmW5Nu/OmSP9GzeU8uaXYJNWJqlEp9TPEVNIppAm4diNUiUgNYDGQCtYE/quqU0qtmYGwUkylVkye74a/du8OcOW4J1zA6fuo4H6z7gCmrpvDZls/IyMogpnIMaRlpdG7YmQd7PMjN8TcTHRWavpOlO5fyzOJnmL7GLTqV2DKRXi160atFL7o36R4Rs4bNmYo7zHW1qsZ7bo8CElX1OhFpCHyiqp1DVeHisgBhSt3778OQIdCpE3z6qevILkVpGWnM2TiHKaum8PGGj0nLSKNFjRYM6TCEoR2Gcl7d85i8cjLPLH6GtQfW0vScpozqPorfdPlNUP6Dz9IsZq2fxTOLn+Gb7d9wTpVzGNFlBPd3v5/mNZoH4RWaUCtugPjRGwREZDbwnqom5d0XSSxAmLCYPdul5zj3XPj884JTkAdBRlYGX2z5gimrpjBz3UyOnTpG/Wr1uTnuZoZeMJQeTXuc0ZyUpVnM3TSXMYvGMH/bfKqfVZ0RF45gZPeRNKvRrMh1SElP4c0Vb/LckufYeGgjzWs0Z1T3Udzd5W4bXlrGFDdAzAeeAXYC84HzVXWPiFQGVqlqxE0rtQBhwubzz93a1C1auNtNmgT16bM0i0U7FjHl5ym8u+ZdDqQcoEaVGgxuP5ihHYbSu1VvKlcKZIl5+GH3Dzyz+BmmrZqGiHBz/M082ONBujQqfO7rnhN7GPf9OMYvG8/B1INc1PgiHuzxIDfE3RDw+U1kKW6AOBd4AWgIjPW5eugL9FHVB0NT3eKzAGHC6ptv3Cp29eu75UuDkB32570/89bKt5i6aio7ju0gtnIs15x3DUM7DKVf234lyv+z/eh2nl/yPK/88ArHTx/n8laX88cef6Rf235nXIGs3reaZxc/y9s/v016ZjrXnnctD/Z4kMuaXxaWNNQmeCxZnzGl5bvvoF8/lw32yy/dOhRFdOzUMaaumsqrP7zK0l1LqVypMn3a9OHWDrdy7XnXUr1KcDPNHk07yis/vMLYJWPZeXwncfXieLDHg9x6wa0s3L6QZxY/w9xNc4mtHMudCXcy+uLRtKvTLqh1MOFT3CuIFwp6UlV9IAh1CyoLECYirFgBV13lZlp//jnExRX6EFVl4faFvPbja7y35j1S0lPoUL8Dd3e+m9s63kbdqnVDXu30zHTeXf0uYxaPYcWeFdkjoBpUa8D93e7nnq73UKdq6XbCm9ArboA4DawC3gV2kSf/kqq+GeR6lpgFCBMxVq+GK6+EjAz47DNISPB72J4Te5j00yRe+/E1NhzcwNlnnc3QDkO5u/PddGvSLSzNN6rKl1u/ZMqqKVza7FJuveBWm9FcjhU3QNQBbgJuATKAacD7qnrY7wMigAUIE1E2boQrroDjx908iR49ADcKae6mubz242vMWj+LTM3k0maX8psuv+GmuJuodpYtbWpKT3HXpD4ITAAmiEgTYCiwWkQeUtW3QlNVY8qRdu1gwQIXJHr2ZNPoYbx+eS3eXDuFXcd3Ub9aff7Q4w8M7zyc8+tG3KBAYwrMxQSAiHTBBYergE+A5aGulDHlRWbzZrz/zqOM//AxvqryOpWWQP+aXRl3yziubnd1yGY1GxMM+QYIEXkSGAisBaYCj6hqRmlVzJiyLD0znXd+fod/LfwXGw5uoHXD1vxfnREMG/s1TZYtg4VvwQsXBX2+hDHBVFAfRBawBUj13OU9UABV1Y6hr17RWB+ECbdTGad4Y8UbPP3t02w7so2Ehgn8tedfub799W7tgvR0ePZZePJJqFwZ/u//4Pe/twWITNgUt5O6wFk+qvpLEOoWVBYgTLikpKfw8vKX+e+i/7Lr+C66N+nOY70eY0C7Af5HIm3Z4gLDp59C167w8svQOeKy15gKoLid1H4DgIhEAUOAiAsQxpS2Y6eO8dLSl3h28bPsT9lPYstEJl03ictbXV7wENXWreGTT+Ddd2HkSBckRo6Ev/894lKHm4or3/X6ROQcEXlERF4UkT7i3I9rdrq59KpoTOQ5lHqIJ756ghZjW/DIF4/QtXFXFt61kPnD5nNF6ysCm78gArfcAuvWwYgR8NxzblLdhx+G/gUYE4CCFnR9CzgP+Bn4DTAPuBEYpKqDCnhcNhHpJyLrRWSTiDzsZ7+IyAue/Ss9I6a8+14XkX0isqpIr8iYENp7Yi8PffYQLca24Mmvn6R3y94s++0y5vx6Dpc2v7R4T1qzJowfD4sWudvXXQfXXw87dgSx5sYUXYFrUqvqBQAi8ipwAGiuqscDeWJPU9Q43PDYZGCpiHykqmt8DusPtPNs3YHxnp8AScCLwKSAX40xQaSqpGWkkZKewv6U/YxfOp6Xf3iZ05mnuSX+Fv7S8y90qN8heCfs0QOWL3dXEk884a4m/vlPt4KddWKbMCgoQKR7b6hqpohsDTQ4eHQDNqnqFgARmYpb59o3QAwCJqnrKV8iIjVFpJGq7lbVBSLSsgjnM8av05mnWbh9IQt+WcCRtCOkpKcEvCk5gzgqV6rM7R1v5+HLHubcOueGprLR0fDnP8NNN7lO7FGjYOJEGDrUXVXEx7umKWNKQUEBopOIHPPcFiDWU/YOcy1sVZAmgO81cjI5VwcFHdME2F1Yxb1EZAQwAqB5c1vByjg7ju7gk02fMGfjHL7Y+gUnTp9AEKpXqU7V6Kq5tmrR1Wh4dsMz7s97zBWtr6BlzZal8wJatXLpOaZPh+efh7/9DR5/3GWHHTzYBYtu3aBSQa3ExpRMQaOYSnpN6+/fnLxjagM5pkCq+jLwMrhhrkV5rCk/Tmee5tvt32YHhdX7VwPQvEZzfn3Br+nftj+Xt7o86KmyQ0rEXUncdBPs2eM6r2fMcPMo/vMfaNw4p7/iV79yVx/BcPw4bNjghuLGxLhlVGvXdj9r1XLzN0yFEMrfdDLgu5ZhU1xW2KIeY4xfyceS+WTjJ8zZNIfPt3zOidMniK4UTc8WPbkz4U76t+1PXL248rGgTcOG8Lvfue3wYbfM6cyZ8MYb8NJL7ov7mmtcsOjTB6pWLfj5MjNh+3ZYvz73tm4d7CrkT7BGjdxBI7+fHTvaTPEyLmQLBnmWJt0AXIFbtnQpcKuqrvY55mrgPmAArvnpBVXt5rO/JfCxqgbUE2gT5cq3zKxMvtn+TXZQWLXPDXBrdk4z+rftz4B2A8reVUJJpaTAvHkuWHz0ERw54oJDv34uWPTu7b7wvV/+3kCwcSOkpeU8T82acN55Odv550ObNm7m98GDbjt0KPfPvPcd9pPoOSHBrbJ39dXQvbt1tkegsK0oJyIDgLFAFPC6qv6fiNwDoKoTxP1r9yLQD0gB7lLVZZ7HTgESgbrAXuBvqvpaQeezAFE+7T+5n1d/eJUJyyew/eh2oitFc1nzyxjQbkD5ukooqfR0+Ppr1wz1wQewO09XXlSU69vwBgDfgFC/fsk7vzMzXZA4dAj274eFC92VzqJFbl/t2i5wXX019O3rrjJM2NmSo6ZM+n7n97z4/YtMWz2N05mn6d2yN/d0vYf+bftXrKuE4sjKgu+/d0ugNm+ec0Vw1lmlX5fDh91Vzpw5bvb4/v2uc/3ii3OuLjp1stFZYWIBwpQZqempTFs9jXFLx7Fs1zLOPutshnUaxu8v+j1x9QpfutNEuKwsWLbMXVnMmeNug+twHzDAbVde6db0NqXCAoSJeFsPb2XCsgm8+uOrHEo9RPu67bn3onu5vdPtnFOlsBHVpszaswfmznUBY948OHbMjcbq2dM1Q/Xp4zq7bThvyFiAMBEpS7P4bPNnvLj0RWZvmE0lqcSg8wdx30X3kdgy0foVKpr0dNdfMXu2y3K7cqW7v359uOqqnK1x4/DWs5yxAGEiyuHUwyStSOKlZS+x6dAm6lerz4guI/hd19/R9Jym4a6eiRS7d8Pnn7sri3nzYN8+d3+HDu7Kok8f6NULYmPDW88yzgKECaqdx3ay9chWUtJTSE1PzZWaIjUjTzk9lZSM3OUf9/xISnoKlzS7hHsvupcb2t9AlcpVwv2yTCTLyoKff84JFt98A6dOQZUqrjnKGzA6drTO7iKyAGFKTFX5dse3jF0ylpnrZpKlWQUeH1M5JjtNRWzl2Jzb0bG0q92O3134Ozo3sgVyTDGlpLgg4Q0YqzxJnxs0cHM/Ond2waJjR2jUyIJGASxAmGI7nXmad1e/y9glY1m+ezm1Y2szossILm91ea5cRbHROUEgpnKMW17TmNKyaxd89lnO1YVvqnTvrG7fLT7emqY8LECYItt3ch8Tl03kpWUvsefEHtrXbc+oi0dxW8fbqBpdSBoHY8Lt8GHXJLVyZc7288/uygPcqKh27c4MHC1aVLirjWItOWoqppV7V/L8kueZ/PNkTmWeon/b/oy6eBRXtb7KRhWZsqNWLdeB3atXzn1ZWS4BoW/Q+OEHeO+9nGOqV3cTClu3PnNr0SI8Ew3DyAKEIUuzmL1hNmO/G8uXW78ktnIswzsP54HuD3B+3fPDXT1jgqNSJZcu3Zsy3ev4cdeHsXKl+7llC6xZ44bbnjqVc5wING16ZuBo1cr9DEa6kghjAaICO37qOEkrknjh+xfYdGgTTc9pylNXPMVvL/wttWNrh7t6xpSO6tXdan49euS+PyvLDbXdutUFDe+2daubp5E3623Vqi57bcOGubcGDXKX69cPXmr2ELMAUcFkaRbf7/yeaaum8fqK1zl26hg9mvbgn73/yeD2g4mOKhsfXGNCrlIl94XfpAlcdtmZ+1NTYdu2nKCxZYsLKHv2uP6Ozz5z2XX9qVv3zCDSoAHUq+cCiO/PwlK3h5AFiAogPTOdr3/5mhlrZ/DBug/YfWI3lStV5sa4GxnVfRTdm+Zd6M8YU6jYWGjf3m35SUuDvXtd0MhvW7jQ/fRNv+6rWjX/gcP3Z4MGbmhvkFmAKKdS0lOYt3keM9fNZNb6WRxOO0zV6Kr0a9uPwecP5upzr6ZmTM1wV9OY8i0mxnVut2hR8HGqcOKEmy2+f3/OT9/b+/a5Zq2ffnK3T5/OeXz9+i4QBZkFiHLkSNoRZm+YzYx1M5i7aS4p6SnUjKnJteddy/XnX0+fNn1siKoxkUjE9YV4R1EVRtV1rnuDR2pqSKplAQLoP7k/1aKr0aJGC5rXaJ5rq1u1bkQP79xzYg8frvuQmetm8sXWL8jIyqDR2Y0Y1mkYg9sP5lctfmX9CsaUNyJwzjlua9s2ZKep8AEiS7PI0ixW7VvFnI1zSM3IHYljKsfkBIxzmp8RQJrVaEZM5Zig10tVOXH6BPtO7mN/yn72n9yf+3bKPjYd2sR3yd+hKG1qtWH0xaMZ3H4w3Zp0s5nMxpgSs5nUPlSVg6kH2X50+xnbL0d/YfvR7ew5seeMx1U/qzqx0bHEVI4htnIssdGxxFb2lH1v+9kHZH/h7z+5n/0pnkBwcj+nMk+dcS6AqtFVqV+tPo2rN6ZP6z4Mbj+YDvU7RPSVjjEmMtlM6gCJCHWr1qVu1bp0adTF7zGnMk6RfCw5V+A4lHqItIw0UjNSSU1PzXX72KljpGZ47ktPzb6dlpEzYqFqdFXqVa1H/Wr1aXh2Qy6ofwH1q9XPvq9etXq5bls/gjGmNFiAKKIqlavQpnYb2tQOoCOpAFmaxamMUyhqX/jGmIhkASJMKkml7CYmY4yJRNaTaYwxxi8LEMYYY/yyAGGMMcYvCxDGGGP8sgBhjDHGLwsQxhhj/LIAYYwxxi8LEMYYY/yyAGGMMcYvCxDGGGP8CmmAEJF+IrJeRDaJyMN+9ouIvODZv1JEugT6WGOMMaEVsgAhIlHAOKA/EAcMFZG4PIf1B9p5thHA+CI81hhjTAiF8gqiG7BJVbeo6mlgKjAozzGDgEnqLAFqikijAB8bVIlJiSStSAIgPTOdxKRE3l75NuDWd05MSmTaqmkAHE07SmJSIjPWzgDgQMoBEpMSmbV+FuBWeUtMSmTuprkA7Di6g8SkRD7f8jkAWw5vITEpka+3fQ3A+gPrSUxKZNGORQCs2reKxKRElu5cCsCKPStITEpkxZ4VACzduZTEpERW7VsFwKIdi0hMSmT9gfUAfL3taxKTEtlyeAsAn2/5nMSkRHYc3QHA3E1zSUxKzF7bYtb6WSQmJXIg5QAAM9bOIDEpkaNpRwGYtmoaiUmJpKSnAPD2yrdJTEokPTMdgKQVSSQmJWa/l68sf4UrJ12ZXX5p6Uv0n9w/u/z8kue5dsq12eUxi8Zww7s3ZJefWvgUQ6YPyS7/4+t/cNuM27LLj89/nLs+vCu7/MjnjzBi1ojs8h/n/ZF7Z9+bXR41dxSj5o7KLt87+17+OO+P2eURs0bwyOePZJfv+vAuHp//eHb5thm38Y+v/5FdHjJ9CE8tfCq7fMO7NzBm0Zjs8rVTruX5Jc9nl/tP7s9LS1/KLl856UpeWf5Kdtk+e/bZ8yrpZy/YQhkgmgA7fMrJnvsCOSaQxwIgIiNEZJmILNu/f3+JK22MMcYJ2YpyInIT0FdVf+Mp3w50U9X7fY6ZDfxbVRd6yl8AfwZaF/ZYf0q6opwxxlQ04VpRLhlo5lNuCuwK8JizAnisMcaYEAplE9NSoJ2ItBKRs4AhwEd5jvkIuMMzmuli4Kiq7g7wscYYY0IoZFcQqpohIvcBnwJRwOuqulpE7vHsnwDMAQYAm4AU4K6CHhuquhpjjDlTyPogwsH6IIwxpmgK6oOwmdTGGGP8sgBhjDHGLwsQxhhj/LIAYYwxxq9y1UktIvuBX4r58LrAgSBWJ1isXkVj9Soaq1fRlMd6tVDVev52lKsAURIisiy/nvxwsnoVjdWraKxeRVPR6mVNTMYYY/yyAGGMMcYvCxA5Xg53BfJh9Soaq1fRWL2KpkLVy/ogjDHG+GVXEMYYY/yyAGGMMcavChUgRKSfiKwXkU0i8rCf/SIiL3j2rxSRLqVUr2YiMl9E1orIahEZ6eeYRBE5KiIrPNvj/p4rBHXbJiI/e855RibEcLxnInKez/uwQkSOicioPMeUyvslIq+LyD4RWeVzX20R+UxENnp+1srnsQV+HkNQr/+KyDrP72mmiNTM57EF/s5DUK8nRGSnz+9qQD6PLe33a5pPnbaJyIp8HhvK98vvd0OpfcZUtUJsuLThm3Gr1Z0F/ATE5TlmAPAJIMDFwHelVLdGQBfP7erABj91SwQ+DsP7tg2oW8D+sLxneX6ve3CTfUr9/QJ6AV2AVT73/Qd42HP7YeDp4nweQ1CvPkBlz+2n/dUrkN95COr1BPDHAH7Ppfp+5dn/DPB4GN4vv98NpfUZq0hXEN2ATaq6RVVPA1OBQXmOGQRMUmcJUFNEGoW6Yqq6W1V/8Nw+DqwlnzW4I1BY3jMfVwCbVbW4M+hLRFUXAIfy3D0IeNNz+03gOj8PDeTzGNR6qeo8Vc3wFJfgVmosVfm8X4Eo9ffLS0QEuBmYEqzzBaqA74ZS+YxVpADRBNjhU07mzC/hQI4JKRFpCXQGvvOzu4eI/CQin4hIfClVSYF5IrJcREb42R/u92wI+f/hhuP9AmigbmVEPD/r+zkm3O/bcNyVnz+F/c5D4T5P09fr+TSXhPP96gnsVdWN+ewvlfcrz3dDqXzGKlKAED/35R3jG8gxISMiZwPvA6NU9Vie3T/gmlE6Af8DPiilal2qql2A/sC9ItIrz/6wvWfilqO9FnjPz+5wvV+BCuf79iiQAUzO55DCfufBNh5oAyQAu3HNOXmF829zKAVfPYT8/SrkuyHfh/m5r0jvWUUKEMlAM59yU2BXMY4JCRGJxn0AJqvqjLz7VfWYqp7w3J4DRItI3VDXS1V3eX7uA2biLlt9he09w/1B/qCqe/PuCNf75bHX28zm+bnPzzFhed9EZBgwEPi1ehqq8wrgdx5UqrpXVTNVNQt4JZ/zhev9qgwMBqbld0yo3698vhtK5TNWkQLEUqCdiLTy/Oc5BPgozzEfAXd4RuZcDBz1XsaFkqeN8zVgrao+m88xDT3HISLdcL+7gyGuVzURqe69jevkXJXnsLC8Zx75/mcXjvfLx0fAMM/tYcCHfo4J5PMYVCLSD3gIuFZVU/I5JpDfebDr5dtndX0+5yv198vjSmCdqib72xnq96uA74bS+YyFouc9UjfciJsNuJ79Rz333QPc47ktwDjP/p+BrqVUr8twl34rgRWebUCeut0HrMaNRFgCXFIK9WrtOd9PnnNH0ntWFfeFX8PnvlJ/v3ABajeQjvuP7W6gDvAFsNHzs7bn2MbAnII+jyGu1yZcm7T3MzYhb73y+52HuF5veT47K3FfYI0i4f3y3J/k/Uz5HFua71d+3w2l8hmzVBvGGGP8qkhNTMYYY4rAAoQxxhi/LEAYY4zxywKEMcYYvyxAGGOM8csChDEFEJF/i8sMe12wM4gWoQ5fiUjQF6Q3pjAWIIwpWHdc7ptfAd+EuS7GlCoLEMb4IW7thJXARcBi4DfAePGzroSI1BOR90VkqWe71HP/EyLyloh86cnb/1vP/eJ5/lWedQRu8XmuP3vu+0lEnvI5zU0i8r2IbBCRniF98cZ4VA53BYyJRKr6JxF5D7gd+APwlapems/hzwPPqepCEWkOfAq09+zriFsnoxrwo4jMBnrgEtN1AuoCS0Vkgee+64DuqpoiIrV9zlFZVbuJW0znb7gUEMaElAUIY/LXGZfa4HxgTQHHXQnEeVI/AZzjzc8DfKiqqUCqiMzHJXK7DJiiqpm4pGtf465UfgW8oZ48Sarquz6BN0nbcqBlCV+XMQGxAGFMHiKSgMvB0xQ4gMv7JOKWnOzh+cL3Vcnf/Z6AkTeXjeI/DTOe+/PLfXPK8zMT+7s1pcT6IIzJQ1VXqGoCOcs7fgn0VdUEP8EBYB4uOSCQHWC8BolIjIjUwS2DuhRYANwiIlEiUg+33OX3nucZLiJVPc/j28RkTKmzAGGMH54v7sPq1ig4X1ULamJ6AOjqWRFtDS6rrNf3wGxcRtl/qFs7YCYuO+dPuODzZ1Xdo6pzcdlMl3muVv4Y7NdlTFFYNldjQkREngBOqOqYcNfFmOKwKwhjjDF+2RWEMcYYv+wKwhhjjF8WIIwxxvhlAcIYY4xfFiCMMcb4ZQHCGGOMX/8fOg9vwS7XZ9gAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load here an example fit\n",
+ "# Use results of the workshop participants\n",
+ "fit2 = fc.RuNFit('runner_fit/fit_hdf5/fit', 9)\n",
+ "#fit2 = fc.RuNFit('MH-df4-2', 7)\n",
+ "figRMSE = fit2.plot_rmse()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For a more detailed analyze, you could have a look at the predicted energies and forces per structure. This is quite useful to identify inaccurately described structures. The training data set may has a specific order of different structures (bulk, slab, cluster, ...) and you can identify, if some parts of the data set are described inaccurately in general. The second plot shows the atomic energy prediction of the NNP over the reference values. For a perfect fit, all points will be on the blue line, but as we can see this is not the case. In this plot, we can identify, if some energies ranges are not well described in our data set. This is related to our first data set analysis above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "figE1, figE2, figF3 = fit2.plot_points()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The same plot can also be done for the atomic forces."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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uLubwww8nvGnI8OHDadq0KeB6Krj66qs57rjj6NatG8cffzwffPABAB07dqRbt24Vv4t4/u7jEq1DsLr0sg4l/Uu4Q8mQSZO8xuGKrNopn6pWdCSZiiZNmqRcRjpNKpvkv8won2Wi2yRTRj4oKyvTkpKSmO9v2LBBV6xYoaqq27dv16OOOkrXrFmjqqolJSX64IMPqqrqkiVL9Nvf/nbFdoMGDdJu3brp888/r6quM8oTTjih4u9z/vz5OmLECN27d6+qqq5bt063bNmiqqpHHHGEbtq0Ka74E+lQ0s5UTPaYPLnmdZKU7q7vL774Yh577LGK+QsvvJBFixalrT41Ce8c0Tef3be/99579OrVq2L+nXfeoXfv3mmLPVu1bdu24nNo1qwZXbp04eOPP95vvX79+u23fOTIkSxYsACAhx9+mPPOO6/ivfLyctq2bUu9eu5Q3759ew455JB0VQOwR4pNHfL222/z9NNPU1BQwPbt23nuueeoX78+Tz/9NL/85S/529/+tt82//vf/ygrK2PHjh107tyZH//4xzRo0ICdO3dWdInRqVMnxo4dy4wZMzjnnHPYtm0bL774IvPmzavlGqafqvLTn/6Uxx57jMLCQh544AGuv/565syZw5QpU/jggw9o2LAhW7dupXnz5owZM6bK4FTPPPMMM2bM4N577wVg6tSpHHzwwaxevZqioiLuvvtuLrnkEu9xjx0LEZ1Kp6yoCGbO9FsmwNq1a1m1ahV9+/bd770nnnhiv16MhwwZwg9/+EP27t3LggULmD17Nr/97W8BuOCCCxgwYADPP/88Q4YM4aKLLqJnz54V2w4ePLiiE8qSkhLGjRuXcvyWVEzmlZe7ru8ByspgsN8ux0PS3fX9lVdeycaNG3n44YcZMWJEzNEl06l8R3nF4FJlH5TlRPftn332GXfffTfTp0/ngQce4OWXX/Yacyb17duXr7/+mi+++IItW7ZUfBGZOnUqQ4cO3W/9L774ghEjRjBz5kwOCmv4+Ytf/IIJEyawceNGXnrppSrbFBQUMGDAAB544AF27txJx44dK95r3749b731FkuWLGHJkiUMGTKEBx98kCFDhgBQVlZGq1atvNbZkorJvHvuARGYPx8uuAAWLkxLYonW9f0jjzzC2rVrKS4ujrpNIl3fX3zxxdx3330sWLCAOXPmeIs7Efe8cg8iwvzz5nPBQxew8PyFXhOLBt23h3raDffPf/6T5557jkWLFvHb3/6WNWvi6yVpxIgRTJ48mZNPPpnevXvTMg0NHtNxRhGP//73v4C7rDp37tyKHrHXrVtXkWDGjBnDmDFj2L17NyNGjODCCy+scgkL4KabbuK8885j1qxZlJSUsGLFiirvjxw5knPPPZfS0tL9YmjYsCHDhg1j2LBhtG7dmkcffbQiqaSDJRWTeRPDeoRt0waWLUvb2UpIOrq+v+SSSzjhhBNo06YNXbt29VJmosJ7123TtA3LNizzmlTCu2/v168fu3fv5u2336ZLly4VTzANGDCA+fPnV3Tfvn379mrLbNSoEUOHDuXHP/4xd911l7dYs1mHDh2qnOmqKpdddhldunThmmuuibpNvXr1+NnPfsa8efP417/+VeVMZ+DAgVx33XWMGjWqyjYrV66kTZs2tGvXjn379vHqq6/SvXv3tNSpIs60lm5MogYPhgkT0r6bdHR937p1a7p06cKll17qpbxUDe40mAn9/X6W6ei+HdyDDSLCaaed5jXeXPHvf/+bv/71ryxZsqTiEd/Fi/fvxlBE+NWvfsW00OXisOXjx4/f71LWxo0bOfvssznuuOPo3r079evX56qrrqp4f/DgwRX7+8EPfuClLlnV9X0mWNf3/tXVru+/+uorunXrxsqVKzn44IMzHU5Oufnmm9m2bVvFDWaTXXK263tjctXTTz/N6NGjueaaayyhJOjcc8/lvffeY8mSJZkOxXhgScUYD0455RQ++uijTIeRkx555JFMh2A8snsqxhhjvLGkYowxxhtLKsYYY7yxpGKMMcYbSyom71jX98Zkjj39ZfJOy5YtK1orl5aWVunQEGDPnj0x++Xq06cPffrs9+j9fqL1/WWMsTMVk23Kyio7l/Qo3V3fA1x++eUVZy6FhYVMTmNX/sZkKztTMdmjrKyyQ8k0SGfX94888gh33nknAB9++CFDhw5NSxfuxmQ7Syoms0pLqw7ONX9+znZ9D7Br1y6+973vcdttt3HEEUekpR7GZDO7/GUyq7QUVN1r7FjXQ3GaROv6/vXXX+fvf/87u3btirpNIl3fg+vG/LzzzuOUU07xE7QxOcaSiskeEyZA69a1sqt0dH1/++23s2PHDq699lov5RmTiyypmOzRtm3VsVXSKB1d399888289tprFTfr77jjDi/lGpNLrOt76/reu7ra9b0x+SqRru/tTMUYY4w3llSMMcZ4Y0kl3UpLMx1BRtT1y6rG5ItE/5cTTioiUjt3UvPF5MlpayWerRo1asTmzZstsRiT41SVzZs306hRo7i3qbHxo4iEN28WoAiYmnB0dVkaW4lno/bt27N+/Xo2bdqU6VCMMSlq1KgR7du3j3v9eFrUb1fVy0MzIvKnZAKrUyJbif/oR2lrJZ6NGjRoQKdOnTIdRlWlpXX2UqQxtanGR4pFpJOqfhA230JVt6Q9slqStkeKp06FTz+F//3PXf5avLhOJZasI+Ja7RtjvIj1SHGNZyqhhCIirVT1s3xKKGkVasRXXg6TJrnuRyypGGPyXCIdSs4BvpuuQPJW27Ywe3amo6i7yssrH5IoK7PEbkyaJfL0l6QtCmPS5Z573KWv+fPdAxNlZZmOyJi8lsiZSq1ckBaR04FbgQLgTlWdEvG+BO+fAXwFXKKqK4P31gI7gL3AnmjX+0wdE96XWJs2dhnSmDRLJKmk/UxFRAqA24FTgfXAMhFZpKpvhK02DDgqePUF/hT8DBmsqp+lO1aTgwYPtoRiTJolcvnrurRFUekE4F1VfV9VvwEWAOdErHMOcI86LwHNRaRtLcRmjDGmBnEnFVV9PXKZiDQUke+JyKOe4jkMWBc2vz5YFu86CjwpIitE5IpYOxGRK0RkuYgstwZ6xhjjTzLdtIiIDBGRu4ENwJnAXE/xRLvEFnkvp7p1+qtqL9wlsitF5KRoO1HV2araR1X7FBYWJh+tMcaYKmpMKiJSIiL/FJFfi8jNwHvAlcDjwBeqeomqPuopnvVAh7D59rjEFdc6qhr6uRF4BHc5zZjcYC3+TR6I50zlGqAEmAz0AYpV9TxVXYh7ysqnZcBRItJJRA4ARgKLItZZBPwgOGP6DrBNVctFpImINAMQkSbAacB+l+xy1tSpMG4crFoF558PZ5yR6YiMb3Ww81GTf+JJKv8HzMMllWXAEhH5m4icH+f2cVPVPcBVwL+AN4GFqrpGRMaIyJhgtcXA+8C7wF+AnwTLWwMviMgrwMvAP1X1CZ/xZVRJiWtv0auX6/LlsssyHZFJhwsugOOPz3QUmWdnbTkr4eGEg3YiJwMX4p7EWgw8oqoP+w8v/XJiOOHIDirnz4dRozIWjvEo8nd7/fVw440ZCydrWF9tWS9W318pjVEvIo1wiWWUqg5PPrzMyYmkEk7EXQabPj3TkRhfrPPR/VlSyXpJdyhZHVXdBTwQvExtuOYaOPTQTEdhfLLORytZX205L6WkYjLgllsyHUHybEyT6lnno/v31bZwoSWWHBP3jXYRuU1ETkxnMCbPhd87yAeWIP2bONFd2h01yiWUZcsyHZFJUCJnKu8AtwRdojwA3K+qq9MSVQ4YOxZWr850FLmmDIozHYNHzxbD0gzHkNcGu9fiTMeRv4qKYOZMv2Um0k3LraraDxgEbAHuFpE3ReQGETnab1gmr3zzNbz7rpve+nlmYzHGpFWqT3/1xA3e1V1VC7xFVYty7umvXBR6uun44+Hqq3P/OnnoZvLMmbBkSW7XxZgkeXv6S0QaAKfjWrsPAZ7FNYw0Jrp8G9PEbiYbE1PcSUVETgVG4TqQfBnXLf0VqvplmmIz+SgfxjTJtyRpjEeJnKn8EpgPjFfVLWmKx5jckg9J0hiP4k4qqjoYKrq+vwg4UlV/IyKHA21U9eV0BWmMMSY3JNMh5B+BfrhLYeDGhL/dW0TGGGNyVjIt6vuqai8RWQWgqp8H3dQbY4yp45I5U9ktIgUEoy2KSCGwz2tUxuQaa11vDJBcUpmFG1XxUBH5HfAC8HuvURmTa/KtCxpjkpRwUlHV+4AJwB+AcmC4qj7oOzBTx9k3f2NyUlIjN6rq/1T1dlW9TVXf9B2UMVn9zT98yN/ycje+TWh5MoYNcyN63n+/+zlsmJ84jckAr8MBG5NRtTG+e1lZ1SF/I1vXJ5NYpk51ZXz/++7n1Kl+YzamFtl4Kia7JDtIU+hgv3Ch/5iiDeccistH6/ru3WHFCpdQVqxIOVxjMinhMxURaSgi3xeRXwY9FN8gIjekIzgTRb7fa0jkm39pqVtXBE4+GWbNSk/r9tJSN7StqhvzINYYH4MHw4QJ/vdvTA5JuJdiEXkC2AasAPaGlqtqTg5JmHO9FNelsbvLytwBPJ4D9bhx7rOZPj29MZWXu8QXfobiS3ExLF3qv1xj0sDnGPXtVfV0DzEZU71E+tWaMMEd7NOtbdv0JBRj8kQyN+pfFJFu3iMx1fPxlFE+y4eD/bPPujOtM87IdCTGJC2ZpDIAWCEib4nIqyLymoi86juwnFdYCAceCD/9qftZWJhaecXFcN997qmj006DE07wEqbJAqFHigF+/nN4//3MxmNMCpJJKsOAo4DTgLOBs4KfJtyf/uR+3nZb1flkPfggdOjg7jF06gT9+6dWXki+3PjP1XoUF8MTT8CqVW6+SZP0PMFmTC1JaTjhfJD2G/W+b6xne3mZksv1GDYMNm6ElSvd/C23wDXXZDYmY2oQ60Z93GcqIvJC8HOHiGwPe+0Qke0+gzVR+Linkqvf5vPd449Xtk855hh4+unMxmNMCuJOKqo6IPjZTFUPCns1U9WD0hdiHjjnnNTvqYTab0DyLbfDG/Dly43/fKkHwKBB8OabsHhxpiMxJmnWTUttWLQITjwxtTImTqxsg7FwYewGePHy0b1INsiXeoC1UTF5wbppSYfmzWHbtsr5Zs3gscdSK3P1ahg92k2vWpVYy+1oXZ/46F4kG+RLPYzJE3ajPl036gsL4csvYedON//d76aWWEI3cz/5BDZsgHr1YO/emrcD10Hhp5+6x5Gvvtqd6diB1xiTAp8t6kMFNgF2qWqcR7Y6ZtMm91PEJYDHH0+tvND2ofsqjRvHv619m88NzZvD1q2ZjsKYlMSdVESkHjASuBA4HvgaaCgim4DFwGxVfSctUeY6VZgyJfnt69evelbSogVs3pxcWYl0fWJqx9Sp7gx02zY48kh3Vvnll5mOypikJHKjvgz4FnAd0EZVO6jqocBA4CVgiohclIYYc09xcWXvueBu0qfS7mDPHncfpWdPN79lS8ohemePKyevpKTyb+WDD2DAgMzGY0wK4r6nIiINVHV3qutkm7TdU/HdoK1lS/dNNnTG0qABfPNN6nEmqrQ0egLJ5caHiYhV/2RFPtQB0KpV5eVTY7JUrHsqiSSVRsAY4NvAa8BdqrrHa5QZUCst6hs0cGcb+/YlX87YsW642Y0b3fwhh2TmjCVW8sj3pBK6RDVzpvtdtGnjrwPLgw6CHTsq5wsK3N+LMVks5Rb1wDygDy6hDANycvyUWhPqUBJg9+7UDrgdO8Ktt1YmFIDPP08pPG+uv76yM8Tzz099KNypU11jxlWr3M9sGVo3/BLV8uXw3nv+Yuzfv/LSJsT/VJ8xWSiRpHKsql6kqn8GzsfdSzHR1K8Pn31W+Thxqtau9VNOKmIlj/B7R3//O7z+emr7CR28e/VyP0tKUivPl9DIj+DOVJo29RNj8+ZVO5Q0JsclklQq7pWk87KXiJwedKv/rohcG+V9EZFZwfuvikiveLdNq/Dr7LEuc4WvE226uLhyWfh06KAdKdb64dOxrv+HrxNt3cjywpPH4sXw7W+79W+8sbLfqsaN3dlZrFjiibVNm8qeA6ZPd/Px1DNW+dHqn+jnVlxctZucMWPg7LOrxhgutG2szzN8eVHR/vFFrlPTdCLr+pqu7m8llbKjnanW9P+RjnqGyk9D2aVLq5+O9X7x3OKElsczHb6tN6oa1ws3dPD24LUD2BM2vT3ecmrYRwHwHnAkcADwCu4MKXydM4DHAQG+A/w33m2jvXr37q1egOqSJapTp4a+00Z/ha8TOQ2qt9yiOmzY/tOxXrHWD5/+4Q9VBwxQXblSdeBA1c6d3fKBA1X796+6LDymeMoOf82fX/lZRFtf1U1XV16i+z/zTNWOHV3dxo5VnTJl/3VGjFC9+uqq9Yyn7GixTJpUdXms/Yd+n7Hqk+rfSuTfTbzr+pqO93eVaNkPPeT+VkF13DjV8vL4/j/SUc/Q36rnsilFl7y/RKe+EH26uvdvefEWHXbvsLiXxzMd2jbxQx7LI4+nwX9/6snA1wvoB/wrbP464LqIdf4MjAqbfwtoG8+20V4pJ5UpU9zBBFTr11ft2rX6A0X4OuHTIpXrHHBA5fQhh1RfXuT60bZt1Ej1uOMql7duvX8Zffu6n/XqqbZoEb280AE5vLzGjV3S6ttX9YgjVHv2jB1Lx46x4w7FGirvtNPcfLt28dezY8eq9Qwtr18/et1rKi/ys+jcuerv/rTT3O/tpJPc+wMGuNjD9xlZRqyyE/lbiZxOZF1f0zX9rYR/9omUHfr9Q9X/rZr+P3zVLdrvxVPZU4rr69jxxymlaP3f1Neut3fdf3py/f2W1ZtcT1tMaaGUopSih0w5pNrlMcuupszO/9c56uGtOrGSSlZ10yIi5wOnq+rlwfzFQF9VvSpsnX8AU1Q11BX/M8BEoGNN24aVcQVwBcDhhx/e+8MPP0wu4NLSqj3/1kUnnOC6nzn3XHjppdrff4MG8P/+H9x7b3r306QJvPii6+bm2Wcrl/frB337uqfCTOpCf09t22Y6Eq9Ki2FycYaDiKFJ/Sa8ePmLdG/dPaHtfIynckM1r18nFE01u4myLDLrxVonnm3dQtXZqtpHVfsUptIlfWmpaykf6nq9fn13E7s64euET4ffNznssMrpk06qOY7w9aNt27Bh5X4OOAC6dau6fZMmcMUVbrpePTjqqOjlhXTuXFnnV1913bX//vfuAB9r21AsBQXVv9+oESxZ4p6Gatas8uGAWPXcvRs++shNd+xYtW6R+4yse02fW/hn8eWXbpyT4uLK77A9e8IXX1T+7gYMqPwcQ/uM/DyjlR1LrL+V8OnQfuJZ19d0rLpF+wwTLTv09zRpUtX/rZr+P3zVLdrvxUPZpUthSll9xh3mltevV5/zu+w/XSAF+y2rRz2OalEZ00lHnFTt8lhlxyrzyz1f8vT7/sbwSeRG/ZdRXgpchjtT8GE90CFsvj2wIc514tnWv/Au6S+91HXaWJ3wdcKnL7/c/TzmGOjevXK6SZPqy4tcP9q2P/hB5X5OPtn944bWARg4EP78Zzd92WWxYxk2zE0XFlbWedcuV+bJJ8O8efD229XHcuqp1b9/8cWub7I5c9yjtuGDV9VUzw8+qFq30PJQ786Rda+pvMjP4umnq56ZzpnjDnChz+L55ys/x9A+I8uIVnYssf5WwqdD+4lnXV/TseoW7TNMtOxu3eCGG9wYRJ98Aq1bu+W+9xNrOrJOHsueePSlTN/ipi8tupTjD9t/enTP0fstu6zXZVzey8V0TMtjaNKgSbXLY5VdXZk+k8p+18PieQHNgF8BHwBTgUOTKSdKufWB94FOVN5s7xqxzplUvVH/crzbRnt5u1E/aVLldKxr5OHrRJseNKhyWfh0rPJirR8+HRlX5Drhy8LXjSwv/Pp2//5uPrLsceNqrkdNsYb2E37jO556RqtbeJ1ivR/P5xZrP+GifY6xyohcnszfSqz91NZ0dX8rqZRdXu7+jkJ/T+XlNf9/pKOeofLTUPaksuqnY70/6O5BCS2PZzp820Th40Y90AK4MUgmpcAhiWwf5z7OAN7GPcl1fbBsDDAmmBbg9uD914A+1W1b08tbUglXUBD9QJEKH+WF/5FXtyya1asrb8Kff777Z1eNnmwS2X+kaAeVeNRUdrz1jOWXv6ysf9eurq7hiS+V8i+/3N2g9vW3kg/sM8h6KScV4KbgYD0RaBrvdtn+SktSUVVt0KDqEyup/pOEP/GSiQPPlCmVB/tWrdzjkaqxk02qsu2gcv31qr16ubgaNVI966zEE1800c5UTjvNW9g5K9t+/2Y/sZJKIvdUfg60Cy57bRCR7SKyI3htT6Cc/Fdaun/XLA0apFbmhx9W3rQEd0OxNsUazviJJyobii1d6m6y5qPwRp6LF7v7UBC98WMiVq/ef9kLLyRfXr6YNCnTEZgkZdUjxZmQtg4lQ6Mtzpjhnlw56KDkB2AqLq76GCuk3utxskpLY/dSvGSJSzaJDHWc6H4yLbzjTF+daEZ2KNmwoXsAwpgslvLIjyJyQzVvq6r+NqnI8lWoB9sZM9yBp107v+WHd0BYm2Id6CdN8jsAWDYmFKj6DdrXt+ntwYl+6LHZhg39lGtMBqT6SDH4faQ4PzRvXrWvrGbN4I03ki9v6VI4/fTKDgyh8lJUItJ5oM7WJOBbrL6dUikv/G/l+uv3H1/FmBwSd1JR1VtCL2A20Bi4FFiA62/LhGzd6gZaCo0jv2OHe+4+VaFbuQcckFzr9bre+j8bhTegLShwlzXLyjIdlTFJS+hur4i0EJEbgVdxl856qepEVd1Yw6Z1z6ZN8NVXbrpePXj88dTKmzq18ttsYWFlw6xU5csZRi7XI/QQxLp1lY0/jclRiXTTchOwDNcrcTdVLVXVz9MWWT5Rdd9GU7FvX+VN4Y8/dn1txau8vPLJschvwfly9pIP9WjbFmbP9vOggzEZkuojxdvtkeIoIscfOfHE1J7UKi52N+aTHcgpNBbI/PlwwQV2ecUYkzaJ3FOpp6qNVbWZqh4U9mqmqgelM8icE35jvVUr+Pe/k7uxHlle6Imv+vXhD3+If/vQ5ZVRoyrbmFR39pJL8qUeEHvQMWNySCKXv6L1ApzwOnXG44+7xnJPPVXZGWEqxo93l73A3ah/5pnkyhk82F1eyZezl3ypB7i2SNOnwxlnZDoSY5IWdzsVoExE/gY8pqofhRaKyAHAAKAEKAPmeo0wVxUWuu7SL7vMtYb/7LPUylu+HC680LV7OfBA2Lw5tfImhj0F3qaNO3vx1cakNuVDPYYNcw1lAX7+cze0gDE5Ku4W9SLSCBgNXIjrCXgr0Ag3jO+TwO2qujotUaZR2lrUP/SQ63J+5073aPE999Q81ko8fLdcN5kV2VtCaDCwUPfuxmSpWC3qk+qmRUQaAK2Anaq6NfXwMidtSSXEV1ce6SrPZN6wYbBxI6xc6eYz1QWPMQnwMfLj4aFpVd2tquW5nlBySj7dkDZVhe6/gZ/7b8ZkUCKPFD8amgjurZh4HHywn3KKi+G+++Cqq+C009xY3vksmcaMZWUwbZr3UGrNoEGul+fFizMdiTFJSySphD/ZZd2yxCvZnokjPfggdOgAt90GnTq5oXbzWaKNGcvK3NNfNQ3n7EOuJy9j0iiRpKIxpk1t6N698hLJ22+7p8DquvDOGE8+GWbNSv+TX7GSl49uYuyRYpMHEkkqPUIt6IHu1qI+TtagLTGJ3DsqLa3sZHPs2PT1mRVP8vLVTcz48XDKKX7KMiYDEmlRXxDWgr5+0Iq+s7Wor4Hvb5+DBuX3t9lkGzNOmACtW6cnpnQmL99d+hiTYamOSftPL1HkO5/fPr/73fz+NhutS5l4tG1btSFkukQmr1SfyvPdpY8xGZbScMIiskpVMzQEoR9paacS2aCtf//Uxh2PLO+qq+D//i/58ow/oWGjjz8err7aJcJk7+usXu2SaadO9gSYyXpeGz+GFfoTVf1jSpFlWNoaP4YatH30keuixVeDttDoj6Gb9iZ7lJVZTwemzki58WOUApsAf04pqnzmu0PJkDlz4NBD/ZRl/Ap11mlMHRZ3h5IiUg8Yiev763jga6ChiGwCFgOzVfWdtESZy4qKXIM2n+WlOoqkMcakSSJnKmXAt4DrgDaq2kFVDwUGAi8BU0TkojTEaIwxJkck0vX9Kaq6O3Khqm4B/gb8Leho0hhjTB2VyJnKf2taIVrSMSZv+WhFb0yeSbbvL2OMr1b0xuSRRC5/FYpIzGdiVdVabJnqlZbat3tj8lwiSaUAaIqdsQCut47VqzMdRY55thiWZjgGH775Gj5aB5RB0efQ/JBMR2RMUoqKYOZMv2UmklTKVfU3fndvTA765FP31apLF1jzBnQ91hKLMYFEkoqdoYTxnd3zWnm5G3/k2ZkwaUn6u6dPu8MrJ8vegGV/sUaPvtml0pyVyI36y9IWhclvyfY8nAusFX162EMQOSuRM5U7gV7ghhNW1RHpCcnknfDeg9u0cf1j5fzZijEmGhtO2NQu+2ZvqpPqUAIm42w4YWMybepUdyBdtcr9nDo10xFlTj5fKq0jErn81SMYNliAxmFDCAugNvqjMUkqKXEPMvTq5ZJKSUmmI8ocu1Sa81IaTyUfpG08FWMSJeKGLDYmB3gdT0VECkWkMPWwqpTZQkSeEpF3gp9RH/wXkdNF5C0ReVdErg1bXioiH4vI6uCVp4O4G2NM9oo7qYhTKiKfAW8Bb4vIJhG5wVMs1wLPqOpRwDPBfGQMBcDtwDDgWGCUiBwbtsoMVS0KXjYeq8ktkyZlOgJjUpbImcpYoD9wvKq2UNVDgL5AfxEZ5yGWc4B5wfQ8YHiUdU4A3lXV91X1G2BBsJ0xuc8a+5k8kEhS+QEwSlU/CC1Q1feBi4L3UtVaVcuDcsuBaGPmHgasC5tfHywLuUpEXhWRObEunwGIyBUislxElm/atMlD6MYYYyCxpNJAVT+LXKiqm4C4BucSkadF5PUor3jPNqJ1FRO6s/kn3MiURUA5cEusQlR1tqr2UdU+hYVebw0ZY0ydlsgjxd8k+V4FVT0l1nsi8qmItFXVchFpC2yMstp6oEPYfHtgQ1D2p2Fl/QX4RzwxGWOM8SeRM5UeIrI9ymsH0M1DLIuA0AP6JcBjUdZZBhwlIp1E5ABgZLAdQSIKORd43UNMJl/Y/QpjakXWtFMRkZbAQlwXsB8B31PVLSLSDrhTVc8I1jsDmIkb32WOqv4uWP5X3KUvBdYCPwrdo6mOtVOpI6wNiDFexWqnEndSERHRGlaOZ51sY0mljrCkYoxXPho/lonIT0Xk8PCFInKAiJwsIvOovHxlTHawDgqNqVWJJJXTgb3A/SJSLiJviMj7wDvAKFzDw7lpiNGY5FkHhcbUqkSe/jpUVf8I/FFEGgCtgJ2qujUtkRnjg3VQaEytSiSpPEowSBewwAbpMjln8GBLKMakmQ3SZYwxxhsbpMsYY4w3NkiXMcYYb+JOKqpakM5AjDHG5L6kBukyxhhjorGkYowxxhtLKsYYY7yxpGKMMcYbSyrGGGO8saRijDHGG0sqxhhjvLGkYowxxhtLKsYYY7yxpGKMMcYbSyrGGGO8saRijDHGG0sqxhhjvLGkYowxxhtLKsYYY7yxpGKMMcYbSyrGGGO8saRijDHGG0sqxhhjvLGkYowxxhtLKsYYY7yxpGKMMcYbSyrGGGO8saRijDHGG0sqxhhjvLGkYowxxhtLKsaY7FRWBtOmZToKkyBLKsaY7FNWBhdcAMcfn+lITIIsqRhjskNpKYi418knw6xZMHhwpqMyCbKkYozJDqWloOpeY8fCsmWZjsgkIWuSioi0EJGnROSd4OchMdabIyIbReT1ZLY3xuSACROgdetMR2GSkDVJBbgWeEZVjwKeCeajmQucnsL2xphs17YtTJyY6ShMErIpqZwDzAum5wHDo62kqs8BW5Ld3hhjTPpkU1JprarlAMHPQ9O1vYhcISLLRWT5pk2bkg7YGGNMVfVrc2ci8jTQJspb19dmHKo6G5gN0KdPH63NfRtjTD6r1aSiqqfEek9EPhWRtqpaLiJtgY0JFp/q9sYYY1KUTZe/FgElwXQJ8Fgtb29MaqwFuDFZlVSmAKeKyDvAqcE8ItJORBaHVhKR+4H/AJ1FZL2IXFbd9sbUCmsBbgxQy5e/qqOqm4EhUZZvAM4Imx+VyPbGpE1pKUyeXDk/f761ADd1XjadqRiTW6wFuDH7saRijA/WAtwYwJKKMX5YC3BjAEsqxhhjPLKkYowxxhtLKsYYY7yxpGKMMcYbSyrGGGO8saRijDHGG1Gt2530isgm4MMkN28FfOYxnGxl9cwvVs/8kql6HqGqhZEL63xSSYWILFfVPpmOI92snvnF6plfsq2edvnLGGOMN5ZUjDHGeGNJJTWzMx1ALbF65herZ37JqnraPRVjjDHe2JmKMcYYbyypJElETheRt0TkXRG5NtPx+CIiHUSkTETeFJE1IvKzYHkLEXlKRN4Jfh6S6VhTJSIFIrJKRP4RzOdjHZuLyEMi8r/gd9ovT+s5Lvh7fV1E7heRRvlSTxGZIyIbReT1sGUx6yYi1wXHpbdEZGhtx2tJJQkiUgDcDgwDjgVGicixmY3Kmz3Az1W1C/Ad4MqgbtcCz6jqUcAzwXyu+xnwZth8PtbxVuAJVT0G6IGrb17VU0QOA64G+qjqcUABMJL8qedc4PSIZVHrFvyvjgS6Btv8MThe1RpLKsk5AXhXVd9X1W+ABcA5GY7JC1UtV9WVwfQO3EHoMFz95gWrzQOGZyRAT0SkPXAmcGfY4nyr40HAScBdAKr6japuJc/qGagPNBaR+sCBwAbypJ6q+hywJWJxrLqdAyxQ1a9V9QPgXdzxqtZYUknOYcC6sPn1wbK8IiIdgZ7Af4HWqloOLvEAh2YwNB9mAhOAfWHL8q2ORwKbgLuDy3x3ikgT8qyeqvoxcDPwEVAObFPVJ8mzekaIVbeMH5ssqSRHoizLq8foRKQp8DdgrKpuz3Q8PonIWcBGVV2R6VjSrD7QC/iTqvYEviR3LwHFFNxPOAfoBLQDmojIRZmNKmMyfmyypJKc9UCHsPn2uNPtvCAiDXAJ5T5VfThY/KmItA3ebwtszFR8HvQHvisia3GXLk8WkXvJrzqC+ztdr6r/DeYfwiWZfKvnKcAHqrpJVXcDDwMnkn/1DBerbhk/NllSSc4y4CgR6SQiB+BujC3KcExeiIjgrsG/qarTw95aBJQE0yXAY7Udmy+qep2qtlfVjrjf3RJVvYg8qiOAqn4CrBORzsGiIcAb5Fk9cZe9viMiBwZ/v0Nw9wLzrZ7hYtVtETBSRBqKSCfgKODl2gzMGj8mSUTOwF2XLwDmqOrvMhuRHyIyAHgeeI3K+w2/xN1XWQgcjvsn/p6qRt48zDkiUgyMV9WzRKQleVZHESnCPYxwAPA+cCnuy2S+1XMy8P9wTy+uAi4HmpIH9RSR+4FiXG/EnwKTgEeJUTcRuR4Yjfssxqrq47UaryUVY4wxvtjlL2OMMd5YUjHGGOONJRVjjDHeWFIxxhjjjSUVY4wx3lhSMSYGEfmDiBSLyPBEe6IWkUIR+W/QPcrAiPfOCpa/IiJviMiPguXDfXZMKiKXiEg7X+UZEw9LKsbE1hfXPmcQru1OIoYA/1PVnqpasW3QW8Fs4GxV7YHrW21p8PZwXK/X+wk6SkzUJbhuS+KW5H6MqWDtVIyJICI3AUNxfUm9B3wL+AB4SFV/E7HuEcAcoBDXeeOlQAtcy+bGwMdAP1XdGazfAvgfcERoWbD8ROAfwLbgNQLXs8GLuG5lFgHdgH+o6kPBNl+oatNgegJwMa7B6uPAclyX6R8DO4F+uFbmfVT1MxHpA9ysqsUiUopLPh2Bz3BDAtyBa1gHrgHdv5P9PE3dYt9KjImgqr8QkQdxB+lrgKWq2j/G6rcB96jqPBEZDcxS1eEicgPuAH5VRNlbRGQR8KGIPINLJPer6ovB8vCkAdBcVQcF83OjBSAiw3BnOX1V9SsRaRHs5ypcbwHLw8qLpTcwQFV3ish8YIaqviAihwP/ArpUt7ExIZZUjImuJ7AaOAbXX1Ys/YDzgum/AtNqKlhVLxeRbriOEMcDp+IuVUXzQByxngLcrapfBeUn0xXJorAzp1OAY8OS0EEi0iwYX8eYallSMSZM0FfWXFzvrp/hBnwSEVlN2GWsasR1PVlVXwNeE5G/4i6tXRJj1S/DpvcQ3AcNOk48IBR2nPut2B5oVM1+6hFfXY3Zj92oNyaMqq5W1SLgbdxN8yXAUFUtinGQfRHX0zHAhcAL1ZUvIk2DTixDioAPg+kdQLNqNl+Lu0wFbvyQBsH0k8BoETkw2EeLGOWFbz+imv08CVRctgsSrTFxsaRiTAQRKQQ+V9V9wDGqWt3lr6uBS0XkVdw9mJ/VVDwwQUTeCs5+JlN5lrIA+EXwuPG3omz7F2CQiLyMezLtSwBVfQJ3I395UOb4YP25wB0islpEGgf7ulVEngf21lCnPiLyqoi8AYypoU7GVLCnv4wxxnhjZyrGGGO8saRijDHGG0sqxhhjvLGkYowxxhtLKsYYY7yxpGKMMcYbSyrGGGO8saRijDHGm/8PH2xvFCmsGGYAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "figF1, figF2, figF3 = fit2.plot_forces()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The ``mode 2`` output gives further information about the parameters of the fitting procedure, about the fitting data and it gives information about the defined SFs and the SFs ranges. These ranges define the known configuration space of your NNP and are used to identify the already mentioned ``extrapolations``. If the NNP is made to predict energies, forces or stress, it first calculates the SF vectors and compares these values to the trainings range shown below. If a SF value occurs, which is not in the range, **RuNNer** will give an ``extrapolation warning`` and tell the user, but you will still get your wanted energy, force or stress. Another usage of these ranges is to increase the data set and the known configuration space."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ " =============================================================\n",
+ " Short range symmetry function values for element Cu\n",
+ " Training set: min max average range stddev range/stddev\n",
+ " 1 6.72393532 25.25447208 14.00001115 18.53053676 4.02011080 4.60945921\n",
+ " 2 5.03976308 19.37796546 10.59714429 14.33820238 3.11321693 4.60559053\n",
+ " 3 3.25577537 13.11073890 6.98744718 9.85496353 2.14525332 4.59384607\n",
+ " 4 1.25883453 6.21227127 3.05195579 4.95343674 1.07630994 4.60224009\n",
+ " 5 0.06568766 1.53442885 0.48718005 1.46874120 0.26713619 5.49809898\n",
+ " 6 2.42989188 42.06637435 12.30898440 39.63648248 7.96564228 4.97593051\n",
+ " 7 6.59284339 109.34982696 33.28285935 102.75698358 21.03610544 4.88479124\n",
+ " 8 0.78142670 19.11390444 5.22756080 18.33247774 3.61016236 5.07802030\n",
+ " 9 5.20079617 86.39735705 26.20143575 81.19656088 16.68008353 4.86787495\n",
+ " 10 0.15497639 7.72411752 1.91198556 7.56914113 1.46079520 5.18152107\n",
+ " 11 3.59136462 60.38094318 17.95854981 56.78957856 11.72399989 4.84387403\n",
+ " 12 0.00059457 1.66933490 0.34750355 1.66874033 0.32743124 5.09646040\n",
+ " 13 0.52030958 18.24849080 4.70826264 17.72818122 3.59567219 4.93042198\n",
+ " -------------------------------------------------------------"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "During ``mode 2`` several files will be printed by **RuNNer** and some are printed every epoch:\n",
+ "- scaling.data: information about the SFs if ``scale_symmetry_functions`` is used\n",
+ "- xxxxxx.XXX.out: weights and biases of the specific epoch xxxxxx for atomic NN of element XXX\n",
+ "- optweights.XXX.out: weights and biases of the epoch with lowest RMSE defined by **RuNNer** for element XXX\n",
+ "- trainpoints.xxxxxx.out, testpoints.xxxxxx.out: optional, giving information about training and test energies of epoch xxxxxx\n",
+ "- trainforces.xxxxxx.out, testforces.xxxxxx.out: optional, giving information about training and test forces of epoch xxxxxx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## RuNNer Mode 3\n",
+ "\n",
+ "**RuNNer** ``mode 3`` is the prediction mode and brings the NNP to application. Via N2P2, NNP can also be used in LAMMPS. For ``mode 3``, the ``input.nn``, ``scaling.data`` (if scaling is used), ``weights.XXX.data`` and the ``input.data``, contaning the structures to predict, are needed. A first application of the NNP in the Cu case is to predict the correct energy-volume behaviour."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job job_a_3_4 was saved and received the ID: 69\n",
+ "The job job_a_3_5 was saved and received the ID: 70\n",
+ "The job job_a_3_6 was saved and received the ID: 71\n",
+ "The job job_a_3_7 was saved and received the ID: 72\n",
+ "The job job_a_3_8 was saved and received the ID: 73\n",
+ "The job job_a_3_9 was saved and received the ID: 74\n",
+ "The job job_a_4_0 was saved and received the ID: 75\n"
+ ]
+ }
+ ],
+ "source": [
+ "pr_ev = pr.create_group(\"E_V_curve\") # Creating a new sub-project within the main project\n",
+ "a_list = np.linspace(3.4, 4.0, 7)\n",
+ "for a in a_list:\n",
+ " job_name = \"job_a_{:.4}\".format(a).replace(\".\", \"_\")\n",
+ " job = pr_ev.create.job.Lammps(job_name, delete_existing_job=False)\n",
+ " job.structure = pr_ev.create_ase_bulk(\"Cu\", a=a)\n",
+ " #job.potential = '2012--Mendelev-M-I--Cu--LAMMPS--ipr1'\n",
+ " job.potential = j.lammps_potential\n",
+ " job.calc_minimize()\n",
+ " job.run()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>id</th>\n",
+ " <th>status</th>\n",
+ " <th>chemicalformula</th>\n",
+ " <th>job</th>\n",
+ " <th>subjob</th>\n",
+ " <th>projectpath</th>\n",
+ " <th>project</th>\n",
+ " <th>timestart</th>\n",
+ " <th>timestop</th>\n",
+ " <th>totalcputime</th>\n",
+ " <th>computer</th>\n",
+ " <th>hamilton</th>\n",
+ " <th>hamversion</th>\n",
+ " <th>parentid</th>\n",
+ " <th>masterid</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>69</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>job_a_3_4</td>\n",
+ " <td>/job_a_3_4</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n",
+ " <td>2021-03-09 09:12:11.908333</td>\n",
+ " <td>2021-03-09 09:12:12.233260</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>70</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>job_a_3_5</td>\n",
+ " <td>/job_a_3_5</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n",
+ " <td>2021-03-09 09:12:12.600059</td>\n",
+ " <td>2021-03-09 09:12:12.930932</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>71</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>job_a_3_6</td>\n",
+ " <td>/job_a_3_6</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n",
+ " <td>2021-03-09 09:12:13.308590</td>\n",
+ " <td>2021-03-09 09:12:13.628758</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>72</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>job_a_3_7</td>\n",
+ " <td>/job_a_3_7</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n",
+ " <td>2021-03-09 09:12:14.004092</td>\n",
+ " <td>2021-03-09 09:12:14.332162</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>73</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>job_a_3_8</td>\n",
+ " <td>/job_a_3_8</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n",
+ " <td>2021-03-09 09:12:14.709127</td>\n",
+ " <td>2021-03-09 09:12:15.039564</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>74</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>job_a_3_9</td>\n",
+ " <td>/job_a_3_9</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n",
+ " <td>2021-03-09 09:12:15.413618</td>\n",
+ " <td>2021-03-09 09:12:15.737637</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>75</td>\n",
+ " <td>finished</td>\n",
+ " <td>Cu</td>\n",
+ " <td>job_a_4_0</td>\n",
+ " <td>/job_a_4_0</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_2/02-runner/runner_fit/E_V_curve/</td>\n",
+ " <td>2021-03-09 09:12:16.124528</td>\n",
+ " <td>2021-03-09 09:12:16.435324</td>\n",
+ " <td>0.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
+ " <td>Lammps</td>\n",
+ " <td>0.1</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " id status chemicalformula job subjob projectpath \\\n",
+ "0 69 finished Cu job_a_3_4 /job_a_3_4 /home/pyiron/ \n",
+ "1 70 finished Cu job_a_3_5 /job_a_3_5 /home/pyiron/ \n",
+ "2 71 finished Cu job_a_3_6 /job_a_3_6 /home/pyiron/ \n",
+ "3 72 finished Cu job_a_3_7 /job_a_3_7 /home/pyiron/ \n",
+ "4 73 finished Cu job_a_3_8 /job_a_3_8 /home/pyiron/ \n",
+ "5 74 finished Cu job_a_3_9 /job_a_3_9 /home/pyiron/ \n",
+ "6 75 finished Cu job_a_4_0 /job_a_4_0 /home/pyiron/ \n",
+ "\n",
+ " project timestart \\\n",
+ "0 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 09:12:11.908333 \n",
+ "1 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 09:12:12.600059 \n",
+ "2 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 09:12:13.308590 \n",
+ "3 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 09:12:14.004092 \n",
+ "4 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 09:12:14.709127 \n",
+ "5 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 09:12:15.413618 \n",
+ "6 day_2/02-runner/runner_fit/E_V_curve/ 2021-03-09 09:12:16.124528 \n",
+ "\n",
+ " timestop totalcputime computer hamilton \\\n",
+ "0 2021-03-09 09:12:12.233260 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "1 2021-03-09 09:12:12.930932 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "2 2021-03-09 09:12:13.628758 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "3 2021-03-09 09:12:14.332162 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "4 2021-03-09 09:12:15.039564 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "5 2021-03-09 09:12:15.737637 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "6 2021-03-09 09:12:16.435324 0.0 pyiron@jupyter-janssen#1 Lammps \n",
+ "\n",
+ " hamversion parentid masterid \n",
+ "0 0.1 None None \n",
+ "1 0.1 None None \n",
+ "2 0.1 None None \n",
+ "3 0.1 None None \n",
+ "4 0.1 None None \n",
+ "5 0.1 None None \n",
+ "6 0.1 None None "
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pr_ev.job_table()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_volume(job):\n",
+ " return job[\"output/generic/volume\"][-1]\n",
+ "def get_energy(job):\n",
+ " return job[\"output/generic/energy_tot\"][-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Analysing the data\n",
+ "vol_list = list()\n",
+ "energy_list = list()\n",
+ "\n",
+ "for job in pr[\"E_V_curve\"].iter_jobs(status=\"finished\"):\n",
+ " vol_list.append(get_volume(job))\n",
+ " energy_list.append(get_energy(job))\n",
+ "\n",
+ "args = np.argsort(vol_list)\n",
+ "vol_list = np.array(vol_list)\n",
+ "energy_list = np.array(energy_list)\n",
+ "plt.plot(vol_list[args], energy_list[args], \"-x\")\n",
+ "plt.xlabel(\"Volume [$\\mathrm{\\AA^3}$]\")\n",
+ "plt.ylabel(\"Energy [eV]\");"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/day_2/03-ace/pacemaker-fit-tutorial.ipynb b/day_2/03-ace/pacemaker-fit-tutorial.ipynb
index 3ca3ba5669d8e9da6883f8ae777a74d279eb7fcf..fbe46d8879d0c63c48ce968317ebc029fd6a415d 100644
--- a/day_2/03-ace/pacemaker-fit-tutorial.ipynb
+++ b/day_2/03-ace/pacemaker-fit-tutorial.ipynb
@@ -28,7 +28,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -39,7 +39,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -83,14 +83,14 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>286</td>\n",
+ " <td>1</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df1_A1_A2_A3_EV_elast_phon</td>\n",
" <td>/df1_A1_A2_A3_EV_elast_phon</td>\n",
- " <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
- " <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
- " <td>2021-02-08 10:33:52.341472</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:53.061360</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>zora@cmti001#1</td>\n",
@@ -101,14 +101,14 @@
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>287</td>\n",
+ " <td>2</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df3_10k</td>\n",
" <td>/df3_10k</td>\n",
- " <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
- " <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
- " <td>2021-02-08 10:33:53.993230</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:55.496691</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>zora@cmti001#1</td>\n",
@@ -119,14 +119,32 @@
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>288</td>\n",
+ " <td>3</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df2_1k</td>\n",
" <td>/df2_1k</td>\n",
- " <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
- " <td>pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
- " <td>2021-02-08 10:33:54.435308</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:56.101883</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>zora@cmti001#1</td>\n",
+ " <td>TrainingContainer</td>\n",
+ " <td>0.4</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>finished</td>\n",
+ " <td>None</td>\n",
+ " <td>df4_2_5eV_25A3_8K</td>\n",
+ " <td>/df4_2_5eV_25A3_8K</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
+ " <td>2021-02-18 19:49:57.547918</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>zora@cmti001#1</td>\n",
@@ -140,33 +158,32 @@
"</div>"
],
"text/plain": [
- " id status chemicalformula job \\\n",
- "0 286 finished None df1_A1_A2_A3_EV_elast_phon \n",
- "1 287 finished None df3_10k \n",
- "2 288 finished None df2_1k \n",
+ " id status chemicalformula job \\\n",
+ "0 1 finished None df1_A1_A2_A3_EV_elast_phon \n",
+ "1 2 finished None df3_10k \n",
+ "2 3 finished None df2_1k \n",
+ "3 4 finished None df4_2_5eV_25A3_8K \n",
"\n",
- " subjob projectpath \\\n",
- "0 /df1_A1_A2_A3_EV_elast_phon /home/yury/PycharmProjects/pyiron-2021/ \n",
- "1 /df3_10k /home/yury/PycharmProjects/pyiron-2021/ \n",
- "2 /df2_1k /home/yury/PycharmProjects/pyiron-2021/ \n",
- "\n",
- " project \\\n",
- "0 pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
- "1 pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
- "2 pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
+ " subjob projectpath project \\\n",
+ "0 /df1_A1_A2_A3_EV_elast_phon /home/pyiron/ datasets/Cu_database/ \n",
+ "1 /df3_10k /home/pyiron/ datasets/Cu_database/ \n",
+ "2 /df2_1k /home/pyiron/ datasets/Cu_database/ \n",
+ "3 /df4_2_5eV_25A3_8K /home/pyiron/ datasets/Cu_database/ \n",
"\n",
" timestart timestop totalcputime computer \\\n",
- "0 2021-02-08 10:33:52.341472 None None zora@cmti001#1 \n",
- "1 2021-02-08 10:33:53.993230 None None zora@cmti001#1 \n",
- "2 2021-02-08 10:33:54.435308 None None zora@cmti001#1 \n",
+ "0 2021-02-18 19:49:53.061360 None None zora@cmti001#1 \n",
+ "1 2021-02-18 19:49:55.496691 None None zora@cmti001#1 \n",
+ "2 2021-02-18 19:49:56.101883 None None zora@cmti001#1 \n",
+ "3 2021-02-18 19:49:57.547918 None None zora@cmti001#1 \n",
"\n",
" hamilton hamversion parentid masterid \n",
"0 TrainingContainer 0.4 None None \n",
"1 TrainingContainer 0.4 None None \n",
- "2 TrainingContainer 0.4 None None "
+ "2 TrainingContainer 0.4 None None \n",
+ "3 TrainingContainer 0.4 None None "
]
},
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -184,7 +201,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -193,7 +210,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -387,7 +404,7 @@
"[105 rows x 5 columns]"
]
},
- "execution_count": 8,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -405,7 +422,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -414,7 +431,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -430,7 +447,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -446,7 +463,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -455,7 +472,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -493,7 +510,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -536,7 +553,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -552,22 +569,22 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "2021-03-08 11:13:01,028 - root - INFO - structure_data is TrainingContainer\n",
- "2021-03-08 11:13:01,036 - root - INFO - Saving training structures dataframe into /home/yury/PycharmProjects/pyiron-2021/pyiron_potentialfit/day_2/03-ace/pacemaker_fit/df1_cut5_pyace_hdf5/df1_cut5_pyace/df_fit.pckl.gzip with pickle protocol = 4, compression = gzip\n"
+ "2021-03-09 09:13:42,018 - root - INFO - structure_data is TrainingContainer\n",
+ "2021-03-09 09:13:42,021 - root - INFO - Saving training structures dataframe into /home/pyiron/day_2/03-ace/pacemaker_fit/df1_cut5_pyace_hdf5/df1_cut5_pyace/df_fit.pckl.gzip with pickle protocol = 4, compression = gzip\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job df1_cut5_pyace was saved and received the ID: 289\n"
+ "The job df1_cut5_pyace was saved and received the ID: 76\n"
]
}
],
@@ -584,7 +601,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -628,17 +645,17 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>289</td>\n",
+ " <td>76</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df1_cut5_pyace</td>\n",
" <td>/df1_cut5_pyace</td>\n",
- " <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
- " <td>pyiron_potentialfit/day_2/03-ace/pacemaker_fit/</td>\n",
- " <td>2021-03-08 11:13:01.402031</td>\n",
- " <td>2021-03-08 11:18:19.154866</td>\n",
- " <td>317.0</td>\n",
- " <td>pyiron@dell-inspiron#1</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_2/03-ace/pacemaker_fit/</td>\n",
+ " <td>2021-03-09 09:13:42.059294</td>\n",
+ " <td>2021-03-09 09:18:39.142853</td>\n",
+ " <td>297.0</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>PaceMakerJob</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -649,23 +666,20 @@
"</div>"
],
"text/plain": [
- " id status chemicalformula job subjob \\\n",
- "0 289 finished None df1_cut5_pyace /df1_cut5_pyace \n",
+ " id status chemicalformula job subjob \\\n",
+ "0 76 finished None df1_cut5_pyace /df1_cut5_pyace \n",
"\n",
- " projectpath \\\n",
- "0 /home/yury/PycharmProjects/pyiron-2021/ \n",
+ " projectpath project timestart \\\n",
+ "0 /home/pyiron/ day_2/03-ace/pacemaker_fit/ 2021-03-09 09:13:42.059294 \n",
"\n",
- " project timestart \\\n",
- "0 pyiron_potentialfit/day_2/03-ace/pacemaker_fit/ 2021-03-08 11:13:01.402031 \n",
- "\n",
- " timestop totalcputime computer \\\n",
- "0 2021-03-08 11:18:19.154866 317.0 pyiron@dell-inspiron#1 \n",
+ " timestop totalcputime computer \\\n",
+ "0 2021-03-09 09:18:39.142853 297.0 pyiron@jupyter-janssen#1 \n",
"\n",
" hamilton hamversion parentid masterid \n",
"0 PaceMakerJob 0.1 None None "
]
},
- "execution_count": 17,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -697,14 +711,14 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -731,14 +745,14 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 17,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -765,12 +779,12 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -797,7 +811,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -806,7 +820,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
@@ -823,7 +837,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -832,7 +846,7 @@
"1"
]
},
- "execution_count": 42,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -843,7 +857,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
@@ -904,7 +918,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -913,7 +927,7 @@
"(2, 3, 7)"
]
},
- "execution_count": 44,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -938,14 +952,14 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1152x1440 with 6 Axes>"
]
@@ -967,7 +981,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -976,7 +990,7 @@
"18"
]
},
- "execution_count": 46,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -994,33 +1008,33 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[BBasisFunctionSpecification(elements=[Cu,Cu], ns=[1], ls=[0], coeffs=[-0.947664,-0.20673]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[2], ls=[0], coeffs=[0.127744,0.0378252]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[3], ls=[0], coeffs=[0.395046,0.128133]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[4], ls=[0], coeffs=[0.490821,0.17195]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[5], ls=[0], coeffs=[0.192457,0.100866]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[6], ls=[0], coeffs=[-0.381239,-0.0435031]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[7], ls=[0], coeffs=[-0.717072,-0.124286]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[1,1], ls=[0,0], coeffs=[0.176241,0.135483]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[1,1], ls=[1,1], coeffs=[0.0335965,0.00542081]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[1,1], ls=[2,2], coeffs=[0.361527,0.101027]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,1], ls=[0,0], coeffs=[0.953892,0.247374]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,1], ls=[1,1], coeffs=[-0.000366411,-0.000581538]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,1], ls=[2,2], coeffs=[0.0654394,0.0146316]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,2], ls=[0,0], coeffs=[0.142427,0.0350483]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,2], ls=[1,1], coeffs=[0.00472481,6.57905e-05]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,2], ls=[2,2], coeffs=[0.00853967,-0.000426002]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu,Cu,Cu], ns=[1,1,1], ls=[0,0,0], LS=[0], coeffs=[-0.0106058,-0.0227657]),\n",
- " BBasisFunctionSpecification(elements=[Cu,Cu,Cu,Cu], ns=[1,1,1], ls=[1,1,0], LS=[0], coeffs=[0.309698,0.0822887])]"
+ "[BBasisFunctionSpecification(elements=[Cu,Cu], ns=[1], ls=[0], coeffs=[-0.95121,-0.202222]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[2], ls=[0], coeffs=[0.14178,0.0409501]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[3], ls=[0], coeffs=[0.436094,0.14006]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[4], ls=[0], coeffs=[0.526956,0.183426]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[5], ls=[0], coeffs=[0.173617,0.0969896]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[6], ls=[0], coeffs=[-0.450901,-0.0592275]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu], ns=[7], ls=[0], coeffs=[-0.712825,-0.117959]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[1,1], ls=[0,0], coeffs=[0.160119,0.116377]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[1,1], ls=[1,1], coeffs=[0.0465253,0.0096226]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[1,1], ls=[2,2], coeffs=[0.399933,0.110766]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,1], ls=[0,0], coeffs=[1.12538,0.30352]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,1], ls=[1,1], coeffs=[-0.0014862,-0.000472396]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,1], ls=[2,2], coeffs=[0.0715307,0.0150598]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,2], ls=[0,0], coeffs=[0.174111,0.043949]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,2], ls=[1,1], coeffs=[0.00779379,-0.000127733]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu,Cu], ns=[2,2], ls=[2,2], coeffs=[0.00818897,-0.000672408]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu,Cu,Cu], ns=[1,1,1], ls=[0,0,0], LS=[0], coeffs=[-0.0093474,-0.0203294]),\n",
+ " BBasisFunctionSpecification(elements=[Cu,Cu,Cu,Cu], ns=[1,1,1], ls=[1,1,0], LS=[0], coeffs=[0.423325,0.126696])]"
]
},
- "execution_count": 47,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -1041,7 +1055,7 @@
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -1075,7 +1089,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
@@ -1084,7 +1098,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
@@ -1093,7 +1107,7 @@
},
{
"cell_type": "code",
- "execution_count": 93,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -1117,64 +1131,20 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>id</th>\n",
- " <th>status</th>\n",
- " <th>chemicalformula</th>\n",
- " <th>job</th>\n",
- " <th>subjob</th>\n",
- " <th>projectpath</th>\n",
- " <th>project</th>\n",
- " <th>timestart</th>\n",
- " <th>timestop</th>\n",
- " <th>totalcputime</th>\n",
- " <th>computer</th>\n",
- " <th>hamilton</th>\n",
- " <th>hamversion</th>\n",
- " <th>parentid</th>\n",
- " <th>masterid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>290</td>\n",
- " <td>aborted</td>\n",
- " <td>Cu4</td>\n",
- " <td>opt_lammps</td>\n",
- " <td>/opt_lammps</td>\n",
- " <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
- " <td>pyiron_potentialfit/day_2/03-ace/test_ace_potential/</td>\n",
- " <td>2021-03-08 11:19:06.338172</td>\n",
- " <td>2021-03-08 11:19:06.828952</td>\n",
- " <td>0.0</td>\n",
- " <td>pyiron@dell-inspiron#1</td>\n",
- " <td>Lammps</td>\n",
- " <td>0.1</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " id status chemicalformula job subjob \\\n",
- "0 290 aborted Cu4 opt_lammps /opt_lammps \n",
- "\n",
- " projectpath \\\n",
- "0 /home/yury/PycharmProjects/pyiron-2021/ \n",
- "\n",
- " project \\\n",
- "0 pyiron_potentialfit/day_2/03-ace/test_ace_potential/ \n",
- "\n",
- " timestart timestop totalcputime \\\n",
- "0 2021-03-08 11:19:06.338172 2021-03-08 11:19:06.828952 0.0 \n",
- "\n",
- " computer hamilton hamversion parentid masterid \n",
- "0 pyiron@dell-inspiron#1 Lammps 0.1 None None "
+ "Empty DataFrame\n",
+ "Columns: []\n",
+ "Index: []"
]
},
- "execution_count": 93,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -1185,7 +1155,7 @@
},
{
"cell_type": "code",
- "execution_count": 94,
+ "execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
@@ -1194,7 +1164,7 @@
},
{
"cell_type": "code",
- "execution_count": 95,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
@@ -1228,7 +1198,7 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>[pair_style pace\\n, pair_coeff * * /home/yury/PycharmProjects/pyiron-2021/pyiron_potentialfit/day_2/03-ace/pacemaker_fit/df1_cut5_pyace_hdf5/df1_cut5_pyace/df1_cut5_pyace.ace Cu\\n]</td>\n",
+ " <td>[pair_style pace\\n, pair_coeff * * /home/pyiron/day_2/03-ace/pacemaker_fit/df1_cut5_pyace_hdf5/df1_cut5_pyace/df1_cut5_pyace.ace Cu\\n]</td>\n",
" <td></td>\n",
" <td>ACE</td>\n",
" <td>df1_cut5_pyace</td>\n",
@@ -1239,14 +1209,14 @@
"</div>"
],
"text/plain": [
- " Config \\\n",
- "0 [pair_style pace\\n, pair_coeff * * /home/yury/PycharmProjects/pyiron-2021/pyiron_potentialfit/day_2/03-ace/pacemaker_fit/df1_cut5_pyace_hdf5/df1_cut5_pyace/df1_cut5_pyace.ace Cu\\n] \n",
+ " Config \\\n",
+ "0 [pair_style pace\\n, pair_coeff * * /home/pyiron/day_2/03-ace/pacemaker_fit/df1_cut5_pyace_hdf5/df1_cut5_pyace/df1_cut5_pyace.ace Cu\\n] \n",
"\n",
" Filename Model Name Species \n",
"0 ACE df1_cut5_pyace [Cu] "
]
},
- "execution_count": 95,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
@@ -1264,7 +1234,7 @@
},
{
"cell_type": "code",
- "execution_count": 96,
+ "execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
@@ -1273,7 +1243,7 @@
},
{
"cell_type": "code",
- "execution_count": 97,
+ "execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
@@ -1282,16 +1252,25 @@
},
{
"cell_type": "code",
- "execution_count": 98,
+ "execution_count": 35,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "<ipython-input-35-d2e1f8892ec0>:1: DeprecationWarning: pyiron_atomistics.atomistics.structure.factory.ase_bulk is deprecated: Please use .bulk or .ase.bulk. It is not guaranteed to be in service in vers. 0.2.2\n",
+ " lammps_job.structure = test_pr.create.structure.ase_bulk(\"Cu\",\"fcc\",cubic=True)\n"
+ ]
+ }
+ ],
"source": [
"lammps_job.structure = test_pr.create.structure.ase_bulk(\"Cu\",\"fcc\",cubic=True)"
]
},
{
"cell_type": "code",
- "execution_count": 99,
+ "execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
@@ -1300,14 +1279,14 @@
},
{
"cell_type": "code",
- "execution_count": 104,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job opt_lammps was saved and received the ID: 290\n"
+ "The job opt_lammps was saved and received the ID: 77\n"
]
}
],
@@ -1317,7 +1296,7 @@
},
{
"cell_type": "code",
- "execution_count": 105,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
@@ -1361,17 +1340,17 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>290</td>\n",
+ " <td>77</td>\n",
" <td>finished</td>\n",
" <td>Cu4</td>\n",
" <td>opt_lammps</td>\n",
" <td>/opt_lammps</td>\n",
- " <td>/home/yury/PycharmProjects/pyiron-2021/</td>\n",
- " <td>pyiron_potentialfit/day_2/03-ace/test_ace_potential/</td>\n",
- " <td>2021-03-08 17:52:19.328134</td>\n",
- " <td>2021-03-08 17:52:20.225226</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_2/03-ace/test_ace_potential/</td>\n",
+ " <td>2021-03-09 09:18:42.055287</td>\n",
+ " <td>2021-03-09 09:18:42.413620</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@dell-inspiron#1</td>\n",
+ " <td>pyiron@jupyter-janssen#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1382,23 +1361,20 @@
"</div>"
],
"text/plain": [
- " id status chemicalformula job subjob \\\n",
- "0 290 finished Cu4 opt_lammps /opt_lammps \n",
+ " id status chemicalformula job subjob projectpath \\\n",
+ "0 77 finished Cu4 opt_lammps /opt_lammps /home/pyiron/ \n",
"\n",
- " projectpath \\\n",
- "0 /home/yury/PycharmProjects/pyiron-2021/ \n",
+ " project timestart \\\n",
+ "0 day_2/03-ace/test_ace_potential/ 2021-03-09 09:18:42.055287 \n",
"\n",
- " project \\\n",
- "0 pyiron_potentialfit/day_2/03-ace/test_ace_potential/ \n",
+ " timestop totalcputime computer hamilton \\\n",
+ "0 2021-03-09 09:18:42.413620 0.0 pyiron@jupyter-janssen#1 Lammps \n",
"\n",
- " timestart timestop totalcputime \\\n",
- "0 2021-03-08 17:52:19.328134 2021-03-08 17:52:20.225226 0.0 \n",
- "\n",
- " computer hamilton hamversion parentid masterid \n",
- "0 pyiron@dell-inspiron#1 Lammps 0.1 None None "
+ " hamversion parentid masterid \n",
+ "0 0.1 None None "
]
},
- "execution_count": 105,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
@@ -1416,7 +1392,7 @@
},
{
"cell_type": "code",
- "execution_count": 113,
+ "execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
@@ -1425,7 +1401,7 @@
},
{
"cell_type": "code",
- "execution_count": 114,
+ "execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
@@ -1434,7 +1410,7 @@
},
{
"cell_type": "code",
- "execution_count": 115,
+ "execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
@@ -1443,7 +1419,7 @@
},
{
"cell_type": "code",
- "execution_count": 116,
+ "execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
@@ -1452,7 +1428,7 @@
},
{
"cell_type": "code",
- "execution_count": 118,
+ "execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
@@ -1461,36 +1437,36 @@
},
{
"cell_type": "code",
- "execution_count": 119,
+ "execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job murn was saved and received the ID: 291\n",
- "The job strain_0_9 was saved and received the ID: 292\n",
- "The job strain_0_92 was saved and received the ID: 293\n",
- "The job strain_0_94 was saved and received the ID: 294\n",
- "The job strain_0_96 was saved and received the ID: 295\n",
- "The job strain_0_98 was saved and received the ID: 296\n",
- "The job strain_1_0 was saved and received the ID: 297\n",
- "The job strain_1_02 was saved and received the ID: 298\n",
- "The job strain_1_04 was saved and received the ID: 299\n",
- "The job strain_1_06 was saved and received the ID: 300\n",
- "The job strain_1_08 was saved and received the ID: 301\n",
- "The job strain_1_1 was saved and received the ID: 302\n",
- "job_id: 292 finished\n",
- "job_id: 293 finished\n",
- "job_id: 294 finished\n",
- "job_id: 295 finished\n",
- "job_id: 296 finished\n",
- "job_id: 297 finished\n",
- "job_id: 298 finished\n",
- "job_id: 299 finished\n",
- "job_id: 300 finished\n",
- "job_id: 301 finished\n",
- "job_id: 302 finished\n"
+ "The job murn was saved and received the ID: 78\n",
+ "The job strain_0_9 was saved and received the ID: 79\n",
+ "The job strain_0_92 was saved and received the ID: 80\n",
+ "The job strain_0_94 was saved and received the ID: 81\n",
+ "The job strain_0_96 was saved and received the ID: 82\n",
+ "The job strain_0_98 was saved and received the ID: 83\n",
+ "The job strain_1_0 was saved and received the ID: 84\n",
+ "The job strain_1_02 was saved and received the ID: 85\n",
+ "The job strain_1_04 was saved and received the ID: 86\n",
+ "The job strain_1_06 was saved and received the ID: 87\n",
+ "The job strain_1_08 was saved and received the ID: 88\n",
+ "The job strain_1_1 was saved and received the ID: 89\n",
+ "job_id: 79 finished\n",
+ "job_id: 80 finished\n",
+ "job_id: 81 finished\n",
+ "job_id: 82 finished\n",
+ "job_id: 83 finished\n",
+ "job_id: 84 finished\n",
+ "job_id: 85 finished\n",
+ "job_id: 86 finished\n",
+ "job_id: 87 finished\n",
+ "job_id: 88 finished\n",
+ "job_id: 89 finished\n"
]
}
],
@@ -1500,12 +1476,12 @@
},
{
"cell_type": "code",
- "execution_count": 120,
+ "execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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gfvEL+PGPoVMnTyzOJcETS+FV5zNOJLlEYxX8nTBU6yOSZloY5CfbPjcC15rZDGB8ND6DETpS/CmAmc2WdDfwDmEo0DPMbF0hzqFtW/juO7j4YmjXDpo08cTinHMpSbUWu8/MuppZczPbJFNiMbOxZvaX2PwpUWLBzI41s75m1s/MDomVYjCzcWa2hZn92Mwqa85cbVtvDY2jRs7Ll8PBB3ticc5Vbo899sj40HZt2GWXygeu3GCDXEYbr5z3LVZNLVrAoEHp+cmTq/+gpXPO1YYXX3yx1t7Lk0s1TZsG8b9Ts2Y1e5LfOVc3zZs3j6222orjjz+efv36cfjhh/Ptt9/y9NNPs+2229K3b19OOukkvv/++zL73XTTTZx11lk/zN9www2cffbZzJs3j969e3PqqafSp08f9tlnH7777jsAZs6cyU477US/fv0YPnw4X375JRBKQ2eddRa77bYbvXv3Zvr06YwYMYItt9ySCy+88If3SJVKVqxYwV577cV2221H3759eeCBihrw1kDSQ2EWw2v77be3qnjmGbOOHc2efNKsSxczCK/LLw/Ln3mmSodzzlXTO++888N06v9hIV7ZzJ071wD7z3/+Y2ZmJ554ol122WXWtWtXe++998zM7Nhjj7UJEyaYmdnuu+9u06dPtxUrVljPnj1t9erVZma2884721tvvWVz5861xo0b2xtvvGFmZiNHjrRbb73VzMz69u1rzz77rJmZXXTRRfbLX/7yh2OOGTPGzMyuvPJK69y5sy1atMhWrVplXbp0saVLl5qZWevWrc3MbM2aNfbVV1+ZmdmSJUtsiy22sNLS0jLbZPus0585M6yC31UvuVRRvLnx0KFw5JHpdW+/XfO+yJxzdU+3bt0YFNWTH3PMMTz99NP06NGDXr16AXD88cfz/PNle7tq3bo1e+65Jw8//DDvvvsua9asoW/fvgD06NGD/v37A7D99tszb948vvrqK5YvX87uu++e8ZiHHHIIAH379qVPnz507tyZ5s2b07NnT+bPn1/mvc2MCy64gH79+jF06FAWLlzIZ599Rj55cqmCTM+xHHNMev0DD8DAgZ5gnGtoqtsc+pRTTuGWW25h4sSJnHjiiT8sb968+Q/TjRs3Zu3atZUeK7VPo0aNyuzfqFGj9fa//fbbWbJkCa+99hozZ85kk002yXsvB55cclTRA5L9+kGfPmH622/h/vvz05uyc65qClkxVplPPvmEl156CYDJkyczdOhQ5s2bxwcffADArbfe+kOJI27HHXdk/vz53HHHHRwZrwbJoG3btrRv354XXngh6zFz8dVXX7HxxhvTtGlTpk2bxscfV9hzfrV5cslBtifvpbKll9tuC/96gnGu4ejduzeTJk2iX79+LFu2jLPOOouJEycycuRI+vbtS6NGjRg9enTGfY844ggGDRpE+/btK32fSZMmce6559KvXz9mzpzJxRdfXK14jz76aGbMmMGAAQO4/fbb2Wqrrap1nKwquhnTkF7Zbuinbt5nu0k/b176GqdRI7PFi6u2v3OuejLdZK5tc+fOtT59+lR7/wMPPNCeeuqpPEZUGH5DP49y7Sts881ht93CdGlpeOYlxUswzrlMli9fTq9evWjZsiV77bVX0uHknSeXLKZPz71Ll+OOS0//619l16USzPTp+Y3POZe8kpISZs2aVfmG5bRr147333+fe+65pwBRJc+TSxZjxuTepcvhh4en9gFmzgzNkuOGDAnHc87ll+Vyx93VSHU+Y08uedK2LQwblp6/9dbkYnGuoWjRogVffPGFJ5gCMgvjubRIXT3nSP5HCYOF5aMjuUcfhQMPDNOdO8P8+enOLZ1z+ecjUdaOikailPSamQ3ItE9RjedS1+2zD2y8MXz+OSxeDE8/HZY55wqjadOmVRod0dUerxbLoyZN4Oij0/Plb+w751xD4cklz449Nj09dSp8801ysTjnXFI8ueRZ//6wzTZh+rvvYMqURMNxzrlEeHLJMwlOOCE9P3FiYqE451xiPLkUwNFHp1uJvfACRH3XOedcg+HJpQA23RT23z897zf2nXMNjSeXAokNzcCkSaHPMeecayg8uRTIQQdBhw5h+pNPvNNK51zD4smlQJo1g6OOSs/fcktioTjnXK3z5FJA8VZj994LX32VWCjOOVerPLkU0LbbhmGQITzzctddycbjnHO1JZHkImmkpNmSSiWt1+mZpO6SVkg6p4L9x0paKGlm9DogWl4i6bvY8msLfS7ZSGVv7N90U3KxOOdcbUqq5DILGAE8X8H6CcBjlRxjgpn1j16PxpZ/GFueedDqWnTMMZDqSPTVV6EaYwo551ydk0hyMbM5ZvZepnWSDgU+AmbXalAF0rEjHHpoev7mmxMLxTnnak1R3XOR1Bo4D7g0h83PlPSWpJsltY8t7yHpDUnPSRqc5b1OkzRD0owlS5bUNPSsTj45PX3rrbB6dUHfzjnnElew5CLpKUmzMryGZdntUkJ114pKDn8NsAXQH1gMXBEtXwx0N7NtgbOBOyRtmOkAZna9mQ0wswGdOnWqyqlV2dCh0K1bmF66FB58sKBv55xziStYcjGzoWa2TYbXA1l22xEYL2ke8CvgAklnZjj2Z2a2zsxKgRuAHaLl35vZF9H0a8CHQK/8nlnVNW7sN/adcw1LUVWLmdlgMysxsxLgSuByM7u6/HaSOsdmhxMaCCCpk6TG0XRPYEvC/ZvEnXhiaD0G8PjjYQhk55yrr5Jqijxc0gJgZ+ARSY/nsM+NsWbL4yW9LektYAhwVrR8N+AtSW8CU4DRZrasAKdQZSUlsNdeYdrMb+w75+o3mVnSMSRuwIABNmPGjIK/z913w09+Eqa7dYO5c9Nd8zvnXF0j6TUzW+9ZRSiyarH6btiw0DQZQrXY45WW15xzrm7y5FKLmjcv29/YDTckFopzzhWUJ5dadsop6emHHoLFi5OLxTnnCsWTSy378Y9h993D9Lp1MHFisvE451wheHJJwGmnpadvvNFHqXTO1T+eXBIwYgRstFGYnjsXnnoq2Xiccy7fPLkkoEULOO649Px11yUXi3POFYInl4TEq8YeeAAWLkwuFuecyzdPLgnp3Rv22CNMr1vn/Y055+oXTy4JGh0byuz662Ht2uRicc65fPLkkqDhw2HjjcP0woXwyCPJxuOcc/niySVBzZqVHUjsmmuSi8U55/LJk0vCTjutbFf8HxXFAAHOOVcznlwSVlIC+++fnvfSi3OuPvDkUgROPz09ffPN8N13ycXinHP54MmlCOy/P/ToEaaXLYM770w2HuecqylPLkWgceOypZerrw6jVTrnXF3lyaVInHRS6BYG4PXX4ZVXko3HOedqwpNLkejQAY48Mj1/9dXJxeKcczXlyaWInHlmevqee+Czz5KLxTnnasKTSxHZbjvYaacwvXq1D4PsnKu7PLkUmXjp5ZprYM2a5GJxzrnq8uRSZEaOhE03DdOLFsGUKcnG45xz1eHJpcg0a1a2WfLf/pZcLM45V12eXIrQT38akgzAyy/Dq68mG49zzlVVIslF0khJsyWVShqQYX13SSsknZPlGD+X9F50nPGx5edL+iBat2+hzqGQNtkERo1Kz3vpxTlX1yRVcpkFjACer2D9BOCxinaWNAQYBvQzsz7AX6LlWwOjgD7AfsA/JTXOY9y15he/SE/ffTcsXpxcLM45V1WJJBczm2Nm72VaJ+lQ4CNgdpZDnA780cy+j473ebR8GHCnmX1vZnOBD4Ad8hZ4Ldp+exg0KEyvWeO9JTvn6paiuuciqTVwHnBpJZv2AgZLekXSc5IGRsu7APNj2y2IlmV6r9MkzZA0Y8mSJTUNvSB++cv09DXXeG/Jzrm6o2DJRdJTkmZleA3LstulwAQzW1HJ4ZsA7YGdgHOBuyUJUIZtM3YBaWbXm9kAMxvQqVOnHM6o9g0fDptvHqaXLoXbbks2Hudc3TF+PEyblp9jTZsWjlcVBUsuZjbUzLbJ8Hogy247AuMlzQN+BVwg6cwM2y0AplrwKlAKdIyWd4tt1xVYlI/zSUKTJmXvvUyYAKWlycXjnKs7Bg6EI46oeYKZNi0cZ+DAyreNK6pqMTMbbGYlZlYCXAlcbmaZunC8H9gTQFIvoBmwFHgQGCWpuaQewJZAnWvIG7/iOPlkaNMmTM+ZE4ZCrorqXHE45+q+IUNCY6CaJJhUYrn77nC8qkiqKfJwSQuAnYFHJFX6kynpxliz5ZuBnpJmAXcCx0elmNnA3cA7wL+BM8xsXWHOonDiVxxt28Ipp6TX/fWvuR+nulcczrn6oSYJpiaJBUDmo1IxYMAAmzFjRtJhlBH/w/boAVtska4Se+st6Ns39/2r88VwztUfVf09yHV7Sa+Z2XrPKkKRVYu5tPgVx9y5cNhh6XWVlV48sTjn4qpSgsnX74cnlyIW/0Lsvnt6+e23w8KFmffxxOKcyySXBJPP3w9PLkUu9YUYOxb69AnL1qyBq65af1tPLM65bLIlmHz/fmRNLpJ2lvQPSW9JWiLpE0mPSjpDUtuav73LReoLMT/2eOh118FXX6XnPbE453IRTzC//z18/XVhfj8qTC6SHgNOAR4n9NPVGdgauBBoATwg6ZD8hOEqM2QITJ0KjaOe0r7+Gq6/Pkx7YnHOVcWQIXDxxXDRRVBSAiNG5P/3o8LWYpI6mtnSrDvnsE1dUIytxSpyzjlwxRVherPNYOJEOPpoTyzOudx9+SX07w+ffBLmS0rgo49Amfo4yaK6rcXGStol24HrQ2Kpa37/e2jfPkwvWhRakXlicc7lyiyMGZVKLC1ahJqQZ5/N7/tkSy7/A66QNE/SnyT1z+9bu+po0QLOPbfsfLwlmXPOZXPTTXDPPen5228Pw6nno6uYuAqTi5ldZWY7A7sDy4CJkuZIujjqcsUlpE+fdPF16VIYNy7ZeJxzdcOcOXDGGen50aPD/ZZ8dBVTXqVNkc3sYzP7k5ltCxwFDAfm5OftXVVNmxb6GzviiPSy3/0OnnkmuZicc8Xvu+/goINg9eow36dP2Qey851gKk0ukppKOljS7YTRId8HDqtkN1cA8VZhEyZA8+Zh+dq14eojn0Va51z9cuSR4aY9hOr0yZOhZcuy2+QzwWRriry3pJsJ3difBjwKbGFmPzGz+2v2tq6qyjc37twZTjghvX6LLfJfZ+qcqx8uvRQeiA12cuWVFfdPmK8Ek63kcgHwEtDbzA42s9vNbGX138pVV0XPsZx7LjSK/oKvvx6qxzzBOOfiJk8OySXl8MPhtNOy75OPBJPthv4QM7vBzJZJ2lXSiQCSOkVjpbhakO0ByS22gFGj0vNPPpn/m3LOubrrySfhuONC82MII9vecENuz7PUNMHkcs/lEsK49udHi5oCPuBuLcjlyfvf/CY9fd990KmTJxjnXPj/P2xYuCcLoXePyZOhXbvcj1GTBJNLx5XDgUOAlQBmtghoU7W3cVWVa5cuffvCIbFOeMaNK0yzQudc3XLbbaGFWMrll8POO1f9OKnfk+nTq7ZfLslltYU+YgxAUuuqh+eqoqp9hV10UXr6rrvgvfc8wTjXkC1aBA8+mJ7fd9/QdVR1DRkCY8ZUbZ9cksvdkq4D2kk6FXgKuKHq4blcTZ9etS5dBgyA/fYL02bhCgWqf8XhnKu71q6Fo44KD1hDaFn6r3+lG//UlpyGOZa0N7APIOBxM3uy0IHVprrUcWVFXnwRBg0K040bw/vvQ8+eycbknKt9F10U+iCEkFCefhr22KMw71WtjiuldHsCM3vSzM41s3PiiSW+jUvWLrvAnnuG6XXr4A9/SDYe51zte+KJst1BXXJJ4RJLZbIVlKZJ+rmk7vGFkppJ2lPSJOD4wobnquLii9PTt9wCc+cmFopzrpYtWgTHHJNudjx0KPz2t8nFky257AesAyZLWiTpHUlzCb0lHwlMMLNbaiFGl6Pdd4fddgvTa9em77045+q3tWtD9y5LloT5TTcNrcVSgwsmIdtDlKvM7J9mNgjYHNgL2NbMNjezU81sZm0F6XIXfxLXSy/ONQwXXwzPPx+mGzWCO+6ATTZJNqac2g+Y2RozW2xmywscj6uhPfZI17GuXZu+seecq58efbTsPdZLLy2OwQNruXGaqw3x0sukSfDhh8nF4pwrnE8+gWOPTc/vuy9ccEFy8cQlklwkjZQ0W1KppPWasUnqLmmFpAof+4kaG7wXHWd8tKxE0neSZkavawt5HsVqt93Kthzz0otz9c/q1eEh6WXLwnyXLuE+S20/z1KRXPoWO1NS+zy/7yxgBPB8BesnEMaOqSimIcAwoJ+Z9QH+Elv9oZn1j16j8xVwXRMvvdx6a3hq3zlXf/z61/DKK2G6SZPwwHTHjsnGFJdLjtsUmC7pbkn75ePZFjObY2YZf+4kHQp8BMzOcojTgT+a2ffR8T6vaUz1za67wt57h+l160J7d+dc/XDnnXD11en5P/0pPOtWTHIZ5vhCYEvgJuAE4H+SLpe0Rb6DifotOw+4tJJNewGDJb0i6TlJA2Prekh6I1o+OMt7nSZphqQZS1Lt9+qZeHXYXXfBm28mF4tzLj/eeQdOOSU9f9hhcNZZycVTkVxbixnwafRaC7QHpqTudWQi6SlJszK8hmV5q0sJz8+sqCSkJlEMOwHnEvo/E7AY6G5m2wJnA3dI2rCCc7rezAaY2YBOnTpV8nZ10w47hC63U+IdXDrn6p4VK8JgXyujYRu33BJuvjm38VlqW5PKNpD0C8KT+EuBG4FzzWyNpEaEByoz9pVpZkOrEc+OwOFR0moHlEpaZWZXl9tuATA1SnqvSioFOprZEiBVVfaapA8JpZy63XFYDVx2Wegd1Qweeghefhl22inpqJxzVWUGJ58Mc+aE+ZYtYcoU2DDj5XPycim5dARGmNm+ZnaPma0BMLNS4KB8BmNmg82sxMxKgCuByzMkFoD7gT0BJPUCmgFLo1EyG0fLexKq8z7KZ4x1Td++4cndlGJppuicq5qrrgo37VOuvRb69UsunsrkklyuBL6RtFHs1RTCjfnqvKmk4ZIWADsDj0h6PId9bow1W74Z6ClpFnAncHxUitkNeEvSm8AUYLSZLatOjPXJpZemu4GYNi0MfeqcqzteeKHseCynnx6GLy5mlXa5L2ke0A34ktDlfjvCvY3PgVPN7LXChlh49aHL/cr89Kdw/fVhetttYcaM4mkP75yr2KJFsP328OmnYX6HHUJXL82bJxsXVLPL/Zh/AweYWUcz6wDsD9wN/Az4Z/7CdIV0ySWhjhbgjTdC6zHnXHFbvRpGjkwnlo4dw32WYkgslckluQwwsx+qrczsCWA3M3sZqAOn6AA226xsc8Xf/jZ8cZ1zhTN+fM2GGT/77DAQIIQWYcOHQ7du+Ymt0HJJLssknSdp8+g1BvgyunFeWuD4XB6NGQMbbRSm586F665LNh7n6ruBA0MXLdVJMJMmwT/+kZ5v1aps45xil0tyOQroSmihdT/h/stRQGPgiEIF5vKvbduygwdddhl8/XVy8ThX3w0ZElp4VTXBvPYajI51XtWsWXikoBh6O85V1uQSlU6uNLOfm9m20evnZrbEzFab2Qe1FKfLk5/9DLpHY4suWQJ//GOy8ThX31U1wXz+eaj+WrUqzDduDPfdl+6Mtq7ImlzMbB3QSVKzWorHFViLFmVHqJwwIXTb7ZwrnFwTzJo1YZv588O8BBMnwgEH1E6c+ZRLtdg84L+SLpJ0dupV4LhcAR15JAyIGg+uWuUPVjpXG3JJMOecA889l54fN67seC11SS7JZRHwcLRtm9jL1VGNGsEVV6Tnb789PPfinCusbAnmllvgb39Lz590Epx/fq2Gl1eVPkT5w4ZSazNbWeB4EtEQHqLMZPhwuP/+ML3bbvDss8XZAZ5z9c20aSHB3H13SDgvvwy7755+PGDw4FCCKfb/jzV6iFLSzpLeAeZE8/8nyR+erAf+9KcwyBCEJ36nTk02HucaingJ5p57YMSIdGIpKYFHHy3+xFKZXPsW2xf4AsDM3iT04eXquF69QuuxlHPOSbdQcc4V1pAhYVjio46CxYvDsjZt4OmnYYMNko0tH3Idz2V+uUXrChCLS8DYsdChQ5ieNw/++tcko3Gu4TALyWXt2jAvhWrqnj0TDStvckku8yXtApikZpLOIaoic3Vf+/bhYcqUyy+HhQuTi8e5hmL8+JBcUlq1qvtVYXG5JJfRwBlAF8IgXf2jeVdPnHoqbLNNmF65sm63UHGuLnjoIfjNb9Lzp5wSnsCvblcxxSjn1mL1WUNtLRb39NMwNDZ26Isvws47JxePc/XVrFmhz7HU/c3Bg+Gpp0IXL+VbkRW7mrYW6yTpAknXS7o59cp/mC5Je+0VmiannHkmrPM7a87l1eefh4u4VGIpKYF77w2JBarfF1kxyqVa7AGgLfAU8Ejs5eqZv/41dA8D8Prr6cHFnHM1t2pV6B/ss8/C/AYbhKqwTp3KbldfEkwuyaWVmZ1nZneb2b2pV8Ejc7WupKRsVzAXXBA6t3TO1YwZHHwwzJ4d5iWYPBn69s28fX1IMLkkl4cl1cFu01x1nHsubLFFmF6+3G/uO5cPJ58c7qukXHEFHHRQ9n3qeoLJJbn8kpBgVkn6WtI3knwUkHpo/Hh46SW46qr0sptuCsuqatq0cDznGroLLww9G6eceir86le57VuXE0ylycXM2phZIzNrYWYbRvMb1kZwrnalRs1r1QoOOSS9fPTo0BV4rlItXgYOzH+MztUlf/976Nk4Zc89w+iSVXmepa4mmFxai0nSMZIuiua7Sdqh8KG52hb/Eo8aBS1bhuVvvQVXXpnbMepaU0rnCuWjj8o+y7LVVjBlCjRtWvVjpf5vTp+ev/gKrdLnXCRdA5QCe5pZb0ntgSfMrN5cl/pzLmWlEsSIEekWY61awTvvwOabV76fJxbX0H3xBeyyC7z/fpjv1Cn0fFxfunZJqdFzLsCOZnYGsArAzL4EfGTKeix1lTR1KvToEZZ9+2149qWiaxFPLM4Fq1bBoYemE0vz5vWrz7Bc5ZJc1khqDBiEhyoJJRlXj6USzJdfppc9/HDmbvk9sTgXlJbCCSfAf/6TXnbrraEU09Dkklz+BtwHbCxpHPAf4PLsu2QnaaSk2ZJKJa1XpJLUXdKKqJPMTPvfJWlm9JonaWZs3fmSPpD0nqR9axJnQzdkSEgmqQcrIZRe4gnHE4tzaeefD3fdlZ7/859h5Mjk4klSLq3FbgfGAH8AFgOHmtk9NXzfWcAI4PkK1k8AHssS00/MrL+Z9QfuBaYCSNoaGAX0AfYD/hmVulw1pUowjaJvyqefhnFfwBOLc3FXX122+f3Pfga//nVy8SStSS4bmdm7wLv5elMzS41qud46SYcCHwGVDqmscIAjgD2jRcOAO83se2CupA+AHYBqPKnhUg4+OIz7cvHFYf7mm6F37zCSpScW50IJ/xe/SM8fckh4Xqw+daFfVTkNFlZbJLUGzgMuzXGXwcBnZva/aL4LEB/YbEG0zNXQRReFMb5TzjsPJk3yxOLcf/8LRx+dbuyy006ha5cmOV26118FSy6SnpI0K8NrWJbdLgUmmNmKHN/mSGBy/G0zbJOxfZOk0yTNkDRjiXeglZO77krffykthccfTzYe55I2e3Yo2ad6Od5yyzBWS6tWycZVDAqWW81saOVbrWdH4HBJ44F2QKmkVWZ2dfkNJTUh3LfZPrZ4AdAtNt8VWFRBfNcD10N4zqUasTY477wTHgBL/Uf629/CszDxEo1zDcX8+bDffukGLhtvDI89Bh07JhtXsSiqajEzG2xmJWZWAlwJXJ4psUSGAu+a2YLYsgeBUZKaS+oBbAm8WsiYG4rUzfv774cDYt2YjhoFK3ItZzpXTyxbBvvuCwuiX58NNgiJJdXpq0souUgaLmkBsDPwiKRKK1gk3Viu2fIoylaJYWazgbuBd4B/A2eYmQ95VUPxVmF77hme2m/XLqz79FM45phEw3OuVq1cCQceCHPmhPmmTeG++2C77ZKNq9j4MMd49y/ZVNTc+NZb4bjj0vN//nO6ibJz9dX334eWYE88EeZT47L85CfJxpWUmnb/4hqobM+xHHNM2Z6TzzsvjKrnXH21bl24oEolFgj3HRtqYqmMJxeXUWUPSEpw3XXpm5elpXWvS3DXcIwfX7PvphmccUb4/5AydmzoscJl5snFrSfXJ+833RRuuCE9//33MGyYJxhXfFJjFVXnu2kGY8aEi6mU4cPTDxW7zDy5uDKq2qXLoYeGIVxTSkvh8MM9wbjiUpMBt8aNg7/8JT2/115hXJaG/PR9Ljy5uB9Ut6+wCRPS3YmvXAldungVmSs+1UkwV10VeqdIGTQI/v3vdF97rmL+EbkfTJ9evb7C2rSB226DxlEXoW+/HZ6FqUuj5rmGoSoJ5rrryo51v/328PTT3q1Lrjy5uB+MGVP9vsJ23hkuuSQ9f9ttsOOO+YnLuXzKJcFMmgSjR6fn+/SB554LA3+53HhycXlzwQWwxx5hurQ0dOb3xReJhuRcRtkSzJ13wkknped79QqdU7ZuXbsx1nWeXFzeNG4cSiwdOoT5hQvDqHylPm6pK0KZEsw994RnuFLf2S22gJdegrZtk4uzrvLk4vKqSxeYODE9//DD4el954pRPMFceikceWR4WBJg881DYtloo2RjrKs8ubi8O/hgOPvs9PwFF4T6aueK0ZAhYaCvsWPTiaVbN3j5ZejUKdHQ6jRPLq4g/vhH2GWXMF1aGnpP/vTTZGNyLpOpU+F3v0vPb7QRvPJKeEjYVZ8nF1cQTZuGwcVS3cN8+mlIMGvWJBuXc3H33BOqxNauDfPt24d/383boO4NlycXVzBdu8Idd6SfZH7uOTj33GRjci7lzjvL3mPp2jU8ozVlij8EnA+eXFxB7b03XHZZev6qq0J3/c4ladKk0FQ+fo/l1VdDg5SadBXj0jy5uIK74IIwHHLKaafB668nF49r2K65pmwT+c03D71JdO6c3sYTTM15cnEFJ8Ett8DWW4f5VatC78l+g9/VtiuugJ/9LD2/xRYhsWyyyfrbeoKpGU8urla0aQP3359+GG3BgtCj8nffJRmVayjMQgeU8dFSt9oqJJZszY09wVSfJxdXa7bcMrQgS/Uo+8orobt+H2nbFVJpKfz85/D736eX9esX7rGkWodl4wmmejy5uFq1777hpn7K5Mll/9M7l09r1oShif/xj/SyHXYIT963aZP7cTzBVJ0nF1frzjgDTj89PX/xxd6CzOXfypVwyCFw++3pZXvsAS+8AK1aVf14nmCqxpOLq3VSKL0MHZpedtJJYawM5/Jh6VLYc88wsFfKQQfBU09Bs2bVP24qwfhYRZXz5OIS0bRpeFitb98wv3ZtaK789tvJxuXqvnnzYNddwz2VlIsuggcfTA9oVxNDhoSxj1x2nlxcYtq2hUcfDQ+uAXz9Ney/P3z8cc2OO20ajB9f8/hc3fPaa7DTTvDee2FegquvDn2H+Zj3tcuTi0tU164hwbRsGeYXLoR99oHPP6/e8aZNC3XiAwfmL0ZXNzz6KOy+O3z2WZhv1ix08XLGGcnG1VB5cnGJ69cvjPuSGpv8/fdDCebrr6t2nFRiufvu6g/X7Oqma64JN+9Xrgzz7drBk0+G74NLRiLJRdJISbMllUoakGF9d0krJJ1Twf53SZoZveZJmhktL5H0XWzdtQU+FZcne+4ZnoFJVV28/nr4sfj229z298TSMK1bB2edFZ66jw/y9eKLsNtuycbW0CVVcpkFjACer2D9BOCxinY2s5+YWX8z6w/cC0yNrf4wtc7MRucrYFd4I0bA9den5597LjzFv2pV9v08sTRM33wDw4fDlVemlw0YEAb56t07sbBcJJHkYmZzzOy9TOskHQp8BMyu7DiSBBwBTM5rgC4xp5xS9mb8k0/C4YfD6tWZt/fEUreMH5+fZ0Tmzg3VqQ89lF42YkS4IPFBvopDUd1zkdQaOA+4NMddBgOfmdn/Yst6SHpD0nOSBmd5r9MkzZA0Y8mSJTWI2uXbueeWHRnwkUdCAimfYDyx1D0DB9b8IcRnn4X+/UOT45Tf/CYM/FWdhyNdYRQsuUh6StKsDK9hWXa7FJhgZityfJsjKVtqWQx0N7NtgbOBOyRtmGlHM7vezAaY2YBOPlB20bnoIvjtb9PzDzwQSjDffx/mPbHUTTV5yt0sNCvea690Y49mzUKP23/4Q7rPOlckzCyxF/AsMCA2/wIwL3otB5YBZ1awbxPgM6Brrsev6LX99tubKz6lpWbnnmsWflbCa999zR57zKxjR7Nnnkk6QlddzzxTtb/hypVmxx5b9ruw6aZmL71U2DhddsAMq+B3tUkt5rFKmdkP1ViSxgIrzOzqCjYfCrxrZgti+3QClpnZOkk9gS0J929cHSTBn/4Umij/4Q9h2eOPwzPPhO77vcRSd8VLMJWVPufOhcMOgzfeSC8bMADuuy88J+WKU1JNkYdLWgDsDDwi6fEc9rmxXLPlUax/I3834C1JbwJTgNFmtixfcbvaJ8G4cTB2bHrZmjVhfunSpKJy+ZBLFdmDD8J225VNLCeeGDqf9MRS3GQ+mAYDBgywGTNmJB2Gy2LatNDxYPy5l622gieeCOOfu7or0/2ztWvD8Nh//nN6u8aNwz2Xn/7Uu3IpFpJeM7P1nlWEImst5lwmqR+fhx+Gf/4zvfzdd2GXXWDWrORiczVXvgTz8cehG5d4YunUCf77Xxg92hNLXVFU91ycK6/8Ve2QIdChAxx1VHgie8ECGDQI7r23bBf+rm5JJZhDDw1/11Q3LhAG93r00fB3d3WHl1xc0aqoufERR4RxOlKdXaZ6U7755mTidDW3YkXoZPLrr8smlpNPDqNGemKpezy5uKJU2XMsQ4eGbj46dgzza9eGH6JzzgnTru545RXYdtuyXf9AuHF/443+/Epd5X82V3RyfUCyXz+YORO22CK97Ior4IADYJm3ESx6q1eHIa4HDYIPPkgvb9Ys9NLw0EM+nHBd5snFFZWqPnnfpUtoprrLLullTz4Z6unfeqtwcbqamTkzdAVz2WXp3oxbtoQ2beCxx0IfZD5efd3mycUVjep26dKmTXju4dhj08s+/BB23NHvwxSb778PpZWBA8sm/759Q3J54IEw/ALUrKsYlzxPLq4o1LSvsEaN4F//Kvuw5apV4T7MiSeWvUnskvHCC/B//xdKK6n7Yi1bhrFYFi+GKVPW/9t7gqm7PLm4ojB9en46obzkEpg4ETbZJL3sllvCU97+nGwyvvgCTj01DN71XmygjV12gWuvDX/3bH97TzB1VEWdjjWkl3dcWf+sWGF23HFlOzps0sTs8svN1q5Nb/enP+WvA8xnngnHc8G6dWbXX2+20UZl/w5t2phdfbXZU09VrfPKqnZ26QqPLB1XesnF1UutW4cSy8SJsMEGYVmqS5FBg+Cdd8KyfIwvAulqvYEDa3ac2pCvAbsgHCc+uFvKSy/BzjvDaaeVbbl3yCHhs996axg1qmqlVS/B1DEVZZ2G9PKSS/32wQdmO+1U9uq5WTOzcePMVq+u+RVxXbuizle8mY7z8cdmRx5Z9rMGs5ISswcfzM/717XPuz4jS8kl8R/2Ynh5cqn/1qwxu+wys6ZNy/7o9elj9txz1f/Bqqs/dPn+gV+61OzXvzZr3rzs59u8udmFF4bxWPLxvvmK3+WHJxdPLi7y9ttmAwbYelfWxx5rds89DeseQD4S6tdfm/3+92Ybbrj+Z3rEEWZz55bd1+9x1S+eXDy5uJg1a8z+/Gez1q3L/hi2bm12wglmHTpU/gNY1xNLSlXPI7X9ww+HxhHlb9aD2Q47mD3/fGHjdsXBk4snF5fBJ5+YHXbY+j+OHTqYbbCB2eOPZ96vviSWlFzP55lnwmdz7LFm7duv/7n16mU2ZUoYnto1DNmSi7cWcw1Wt27hwb3HH4dttkkv/+KL0Evv/vvDmDFh5MuUmj7sGVcbrbZykUsrrH/9K/TZ9s03cOut8OWX6XU9esBNN8Hs2WE4Yh9vxQFecjEvuTgLz77ccIPZppuuf0W+8cZmEyaYPfJIfkssxXZzu/xx1q41e/TRUM1V/jMBs549zW66KbS4cw0TXi3mycXlZsWKcJO4Q4f1f0ylcJP6gw/y937F1iz3mWfCfZQTTjDr3j1zUtl2W7M77wz3rlzD5snFk4uroq+/Ds/BdOyY+Qd2331D67Lvvqv5exVDM+jly81uvtlsr70yn69kdvDBZk8+6fdUXFq25KKwvmEbMGCAzfCOp1wG//43jBwJTZuWvc+Q0q5dWD9qVOg7q0k1Bw6v6r2cfNz7+eKLMGbK1KnwxBOhx+LyWraE008PnUvGx81xDkDSa2Y2INM6v6HvXAWmTQvd+D/4ICxZAuPGhYGs4pYvhxtugL32Cp1lnnBCaCRQ1cHKqtK1SXUTy9q18OqroVfiQYNg441Dj9EPPbR+YmnaFEaMCN3oHHSQJxZXDRUVaRrSy6vFXHkVVTml7kkce2y4oZ2pCilVjTRwoNmYMWb33Wf26ac1e99c18ctWxaqscaNM9tvv9BhZEXxgln//majR4fzSx2/vjW7dvmFV4tl59ViLq6ykkFq/V13QYsWMHky3H8/LFiQ/bjdu4dBsfr1g969Q2mgZ89Q4ok3363o/TMtLy0NY6F8/HEYIG3OnNAx5KxZYT4bKQyodthhMHw4fPJJ7u/rHGSvFvPkgicXl5brD2n57czCeDEPPhiGWZ4+Pfzw56JZs5BgNtkEOnQIvTh/8w08/zzsvTd07Qrz56eHb27RIlTTLVkCn39e9jmcynTpEkZ63H//cOyOHXM7b08wLpNsySWRaihgJDAbKAUGZFjfHVgBnFPB/v2Bl4GZwAxgh9i684EPgPeAfXOJx6vFnFn1u0LJtP2yZWZTp5qdc47ZoEHrd+hYG6+mTc22287stNPMbrnF7KOPMrf0qsoT+l5F5uIotmoxSb0JieW6KIHMKLf+3mj9K2b2lwz7PwFMMLPHJB0AjDGzPSRtDUwGdgA2A54CepnZumzxeMnFVffKPNf91qwJozC+/XZ4/e9/8NFH4bV8ec1i79ABSkpg881hq63CWCm9e4d/W7TIT/zV3d7Vb9lKLtVsOFkzZjYHQBn6iZB0KPARkG3UcwM2jKbbAoui6WHAnWb2PTBX0geERPNSXgJ39VJNfjDjrbyy7d+0aehiZptt4Mgjy65buTJUb332WWhltnJl6H7mzTdDtyqDB4fx5888E7bfPrTg6tQp/WrVqvbOO9fzdS6RarHUC3iWWLUY0JqQCDYAxlJxtVhv4BNgPrAQ2DxafjVwTGy7m4DDKzjGaYQqtRndu3fPQwHR1UXF1gVLRccr9PFre39XP5BEx5WSnpI0K8NrWJbdLiVUd62o5PCnA2eZWTfgLEISAcjUZV7Gej8zu97MBpjZgE6dOlV2Oq6emj49P1fgqSv66dNrHlOmEkU+h/jNR9WWDznsKpNoazFJzxK75yLpBaBbtLod4b7LxWZ2dbn9vgLamZkp1K19ZWYbSjofwMz+EG33ODDWzLJWi/k9F1csaqPV1vjxMHBgfqq0pk0LCXXMmJofy9U9RXfPpSJmNjg1LWkssKJ8YoksAnYnVKvtCfwvWv4gcIekvxJu6G8JvFrAkJ3Lm1wSRz7ueeQzEQwZ4vddXGaJdP8iabikBcDOwCNRCaOyfW6UlMqQpwJXSHoTuJxw/wQzmw3cDbwD/Bs4wyppKeZcMahKicSrpFxd4A9R4tViLlmFbgbtXKF4x5XOFal8NYP2EowrNp5cnEuIt9py9ZlXiwGSlgAfJx1HFXUEliYdRILqwflvtgms/Ba++qaKO2Y497ZtoHUrWPRZvqIrYvXgb19txXbum5tZxmc5PLnUUZJmVFTX2RA05PNvyOcODfv869K5e7WYc865vPPk4pxzLu88udRd1ycdQMIa8vk35HOHhn3+debc/Z6Lc865vPOSi3POubzz5OKccy7vPLnUEZIaS3pD0sPllp8jySR1TCq22pDp/CX9XNJ7kmZLGp9kfIVU/twl9Zf0sqSZkmZI2iHpGAtF0jxJb6fONVq2kaQnJf0v+rd90nEWSgXn/2dJ70p6S9J9ktolHGZGnlzqjl8Cc+ILJHUD9iYMnFbflTl/SUMII4/2M7M+wHrDYdcj5f/244FLzaw/cHE0X58NMbP+sec7fgM8bWZbAk9H8/VZ+fN/EtjGzPoB7wPnJxdaxTy51AGSugIHAjeWWzUBGEMFA6LVFxWc/+nAHy0MaY2ZfZ5EbIVWwblXNMx3QzEMmBRNTwIOTS6U2mdmT5jZ2mj2ZaBrkvFUxJNL3XAlIYmUphZIOgRYaGZvJhVULbqScucP9AIGS3pF0nOSBiYSWeFdyfrn/ivgz5LmE0psRXnlmicGPCHpNUmnRcs2MbPFANG/GycWXeFlOv+4k4DHajmmnHhyKXKSDgI+N7PXYstaAb8lVInUa5nOP9IEaA/sBJwL3B2NSlpvZDn3iob5ro8Gmdl2wP7AGZJ2SzqgWlbh+Uv6LbAWuD2p4LIpqpEoXUaDgEMkHQC0IFSH3Ar0AN6Mfk+7Aq9L2sHMPk0s0sJY7/wl3QYsAKZaeFDrVUmlhE79liQXat5VdO4HE+7DANzD+tWl9YaZLYr+/VzSfcAOwGeSOpvZYkmdgXpZJQoVnv/zko4HDgL2siJ9WNFLLkXOzM43s65mVgKMAp4xs8PMbGMzK4mWLwC2q4eJpaLzPwa4nzDENZJ6Ac0ort5iayzLuaeG+Yayw3zXK5JaS2qTmgb2AWYRhjM/PtrseOCBZCIsrIrOX9J+wHnAIWb2bZIxZuMlF1dX3QzcLGkWsBo4vliv4ArgVOAqSU2AVUTDfNdDmwD3RaXzJsAdZvZvSdMJ1aAnE1pKjkwwxkKq6Pw/AJoDT0brXjaz0cmFmZl3/+Kccy7vvFrMOedc3nlycc45l3eeXJxzzuWdJxfnnHN558nFOedc3nlycc45l3eeXJxzzuWdJxfnGgBJf5f0eqqDT0m9JV0raYqk05OOz9U/nlycq+eirkM2Bn5K6I8KM5sTPdV9BDAgy+7OVYsnF+dyIOlZSfuWW/YrSf/Mss+Kwke23nu2jIYgaJxaZmYrgc7As8DfYtseAvyHMOAWkppJej7qVsa5GvHk4lxuJhM6j4wbFS0vJicReotel1ogqQPQCvgG+GG5mT1oZrsAR0fzqwmJ5ie1GrGrlzy5OJebKcBBkpoDSCoBNgP+I+lsSbOi16/K7yipJOpgMzV/jqSxsXXvSrox2v92SUMl/TcaI36HaLtjJL0ajaV+XbxkUs7RrN9L8IWEQcVmA1tHx9tD0t8kXQc8Gtv2/ugYztWIJxfncmBmXwCvAvtFi0YBdwHbAScCOxIGLjtV0rZVPPyPgKuAfsBWwFHArsA5wAWSehNKE4PMrD+h9LFeApDUDOhpZvNiy0qAXaJY5wB9ovN51sx+YWY/NbN/xA4zC6ivo3q6WuTJxbncxavGUlViuwL3mdlKM1sBTAUGV/G4c83sbTMrJZQuno6GD3gbKAH2ArYHpkuaGc33zHCcjsDycst+D/wuOt4PyaUiUXXa6tQ4Is5Vl9+4cy539wN/lbQd0NLMXs9x2N21lL2Qa1Fu/fex6dLYfCnh/6iASWZ2fiXv81382JL6AyOAXSX9I1r3dg7xNieME+NctXnJxbkcRSWTZwkDlaVu5D8PHCqpVdTkdzjwQrldPwM2ltQhumdzUBXf+mngcEkbA0jaSNLmGeL7EmgsKZVg/gQcHBux9P+opOQS3fxfYmZrqhijc2V4ycW5qplMqPoaBRCVXm4h3I8BuNHM3ojvYGZrJP0OeAWYC7xblTc0s3ckXQg8IakRsAY4A/g4w+ZPEEoqpUBrM3s6dpzPoqFzNzKzZRW83RDK3uB3rlp8JErn6pGoMcHZZnZsNfefCpxvZu/lNzLX0Hi1mHP1SFRqmpalqXKFotZm93ticfngJRfnnHN55yUX55xzeefJxTnnXN55cnHOOZd3nlycc87lnScX55xzeefJxTnnXN79Pz82c+SCpxqiAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1522,16 +1498,16 @@
},
{
"cell_type": "code",
- "execution_count": 122,
+ "execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "159.1806540485084"
+ "157.26478378968022"
]
},
- "execution_count": 122,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -1542,16 +1518,16 @@
},
{
"cell_type": "code",
- "execution_count": 124,
+ "execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "4.3113630248310635"
+ "5.871581007824487"
]
},
- "execution_count": 124,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
@@ -1562,16 +1538,16 @@
},
{
"cell_type": "code",
- "execution_count": 127,
+ "execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "-3.7003977945319573"
+ "-3.6992805045165476"
]
},
- "execution_count": 127,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@@ -1582,16 +1558,16 @@
},
{
"cell_type": "code",
- "execution_count": 128,
+ "execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "11.972878760885306"
+ "11.966449592242205"
]
},
- "execution_count": 128,
+ "execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
@@ -1623,7 +1599,7 @@
},
{
"cell_type": "code",
- "execution_count": 71,
+ "execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
@@ -1632,7 +1608,7 @@
},
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
@@ -1648,7 +1624,7 @@
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": 52,
"metadata": {},
"outputs": [
{
@@ -1657,7 +1633,7 @@
"1"
]
},
- "execution_count": 73,
+ "execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
@@ -1668,7 +1644,7 @@
},
{
"cell_type": "code",
- "execution_count": 74,
+ "execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
@@ -1684,7 +1660,7 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 54,
"metadata": {},
"outputs": [
{
@@ -1693,7 +1669,7 @@
"(4, 5, 15)"
]
},
- "execution_count": 75,
+ "execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
@@ -1704,7 +1680,7 @@
},
{
"cell_type": "code",
- "execution_count": 88,
+ "execution_count": 55,
"metadata": {},
"outputs": [
{
@@ -1728,10 +1704,8 @@
},
{
"cell_type": "code",
- "execution_count": 76,
- "metadata": {
- "scrolled": false
- },
+ "execution_count": 56,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1757,7 +1731,7 @@
},
{
"cell_type": "code",
- "execution_count": 83,
+ "execution_count": 57,
"metadata": {},
"outputs": [
{
@@ -1766,7 +1740,7 @@
"208"
]
},
- "execution_count": 83,
+ "execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
@@ -1791,7 +1765,7 @@
},
{
"cell_type": "code",
- "execution_count": 84,
+ "execution_count": 58,
"metadata": {},
"outputs": [
{
@@ -1819,7 +1793,7 @@
" BBasisFunctionSpecification(elements=[Al,Al,Al], ns=[1,1], ls=[4,4], coeffs=[-0.000231509,-0.00476812])]"
]
},
- "execution_count": 84,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
@@ -1837,10 +1811,8 @@
},
{
"cell_type": "code",
- "execution_count": 85,
- "metadata": {
- "scrolled": false
- },
+ "execution_count": 59,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1867,7 +1839,7 @@
" BBasisFunctionSpecification(elements=[Al,Al,Al,Al,Al,Al], ns=[1,1,1,1,1], ls=[1,1,1,1,0], LS=[0,0,0], coeffs=[3.28251e-08,-4.23939e-06])]"
]
},
- "execution_count": 85,
+ "execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
@@ -1878,7 +1850,7 @@
},
{
"cell_type": "code",
- "execution_count": 79,
+ "execution_count": 60,
"metadata": {},
"outputs": [
{
@@ -1927,7 +1899,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.6"
+ "version": "3.8.6"
}
},
"nbformat": 4,
diff --git a/day_3/ex_01_validation.ipynb b/day_3/ex_01_validation.ipynb
index 1a55c5f0e24e0705078a2caad2cb07e204f2360e..52adbd2619ea58c708746f88d704a7b77c38e06c 100644
--- a/day_3/ex_01_validation.ipynb
+++ b/day_3/ex_01_validation.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "modified-armor",
+ "id": "removable-insert",
"metadata": {},
"source": [
"# [**Workflows for atomistic simulations**](http://potentials.rub.de/) "
@@ -10,7 +10,7 @@
},
{
"cell_type": "markdown",
- "id": "immediate-glass",
+ "id": "median-patch",
"metadata": {},
"source": [
"## **Day 3 - Validation of various potentials**\n",
@@ -30,7 +30,7 @@
},
{
"cell_type": "markdown",
- "id": "arbitrary-performance",
+ "id": "worth-monitoring",
"metadata": {},
"source": [
"## **Importing the modules and creating the project**"
@@ -39,7 +39,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "peripheral-anime",
+ "id": "welsh-lafayette",
"metadata": {},
"outputs": [],
"source": [
@@ -52,7 +52,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "considered-width",
+ "id": "western-waterproof",
"metadata": {},
"outputs": [],
"source": [
@@ -62,7 +62,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "tutorial-oklahoma",
+ "id": "historic-murray",
"metadata": {},
"outputs": [],
"source": [
@@ -72,7 +72,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "sixth-england",
+ "id": "numerous-engagement",
"metadata": {},
"outputs": [],
"source": [
@@ -87,7 +87,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "charged-typing",
+ "id": "educational-fourth",
"metadata": {},
"outputs": [],
"source": [
@@ -109,7 +109,7 @@
},
{
"cell_type": "markdown",
- "id": "proved-extent",
+ "id": "hidden-lodging",
"metadata": {},
"source": [
"## **Step 1: Get the equilibrium Cu lattice constant determined by the potentials**\n",
@@ -120,7 +120,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "negative-motel",
+ "id": "rational-operations",
"metadata": {},
"outputs": [],
"source": [
@@ -132,42 +132,126 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "variable-bidder",
+ "id": "local-austin",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job murn_job was saved and received the ID: 4659\n",
- "The job strain_0_9 was saved and received the ID: 4660\n",
- "The job strain_0_92 was saved and received the ID: 4661\n",
- "The job strain_0_94 was saved and received the ID: 4662\n",
- "The job strain_0_96 was saved and received the ID: 4663\n",
- "The job strain_0_98 was saved and received the ID: 4664\n",
- "The job strain_1_0 was saved and received the ID: 4665\n",
- "The job strain_1_02 was saved and received the ID: 4666\n",
- "The job strain_1_04 was saved and received the ID: 4667\n",
- "The job strain_1_06 was saved and received the ID: 4668\n",
- "The job strain_1_08 was saved and received the ID: 4669\n",
- "The job strain_1_1 was saved and received the ID: 4670\n",
- "job_id: 4660 finished\n",
- "job_id: 4661 finished\n",
- "job_id: 4662 finished\n",
- "job_id: 4663 finished\n",
- "job_id: 4664 finished\n",
- "job_id: 4665 finished\n",
- "job_id: 4666 finished\n",
- "job_id: 4667 finished\n",
- "job_id: 4668 finished\n",
- "job_id: 4669 finished\n",
- "job_id: 4670 finished\n",
+ "The job murn_job was saved and received the ID: 90\n",
+ "The job strain_0_9 was saved and received the ID: 91\n",
+ "The job strain_0_92 was saved and received the ID: 92\n",
+ "The job strain_0_94 was saved and received the ID: 93\n",
+ "The job strain_0_96 was saved and received the ID: 94\n",
+ "The job strain_0_98 was saved and received the ID: 95\n",
+ "The job strain_1_0 was saved and received the ID: 96\n",
+ "The job strain_1_02 was saved and received the ID: 97\n",
+ "The job strain_1_04 was saved and received the ID: 98\n",
+ "The job strain_1_06 was saved and received the ID: 99\n",
+ "The job strain_1_08 was saved and received the ID: 100\n",
+ "The job strain_1_1 was saved and received the ID: 101\n",
+ "job_id: 91 finished\n",
+ "job_id: 92 finished\n",
+ "job_id: 93 finished\n",
+ "job_id: 94 finished\n",
+ "job_id: 95 finished\n",
+ "job_id: 96 finished\n",
+ "job_id: 97 finished\n",
+ "job_id: 98 finished\n",
+ "job_id: 99 finished\n",
+ "job_id: 100 finished\n",
+ "job_id: 101 finished\n",
+ "Potential: 2012--Mendelev-M-I--Cu--LAMMPS--ipr1\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job murn_job was saved and received the ID: 102\n",
+ "The job strain_0_9 was saved and received the ID: 103\n",
+ "The job strain_0_92 was saved and received the ID: 104\n",
+ "The job strain_0_94 was saved and received the ID: 105\n",
+ "The job strain_0_96 was saved and received the ID: 106\n",
+ "The job strain_0_98 was saved and received the ID: 107\n",
+ "The job strain_1_0 was saved and received the ID: 108\n",
+ "The job strain_1_02 was saved and received the ID: 109\n",
+ "The job strain_1_04 was saved and received the ID: 110\n",
+ "The job strain_1_06 was saved and received the ID: 111\n",
+ "The job strain_1_08 was saved and received the ID: 112\n",
+ "The job strain_1_1 was saved and received the ID: 113\n",
+ "job_id: 103 finished\n",
+ "job_id: 104 finished\n",
+ "job_id: 105 finished\n",
+ "job_id: 106 finished\n",
+ "job_id: 107 finished\n",
+ "job_id: 108 finished\n",
+ "job_id: 109 finished\n",
+ "job_id: 110 finished\n",
+ "job_id: 111 finished\n",
+ "job_id: 112 finished\n",
+ "job_id: 113 finished\n",
+ "Potential: 2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job murn_job was saved and received the ID: 114\n",
+ "The job strain_0_9 was saved and received the ID: 115\n",
+ "The job strain_0_92 was saved and received the ID: 116\n",
+ "The job strain_0_94 was saved and received the ID: 117\n",
+ "The job strain_0_96 was saved and received the ID: 118\n",
+ "The job strain_0_98 was saved and received the ID: 119\n",
+ "The job strain_1_0 was saved and received the ID: 120\n",
+ "The job strain_1_02 was saved and received the ID: 121\n",
+ "The job strain_1_04 was saved and received the ID: 122\n",
+ "The job strain_1_06 was saved and received the ID: 123\n",
+ "The job strain_1_08 was saved and received the ID: 124\n",
+ "The job strain_1_1 was saved and received the ID: 125\n",
+ "job_id: 115 finished\n",
+ "job_id: 116 finished\n",
+ "job_id: 117 finished\n",
+ "job_id: 118 finished\n",
+ "job_id: 119 finished\n",
+ "job_id: 120 finished\n",
+ "job_id: 121 finished\n",
+ "job_id: 122 finished\n",
+ "job_id: 123 finished\n",
+ "job_id: 124 finished\n",
+ "job_id: 125 finished\n",
"Potential: Cu-ace\n"
]
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -181,35 +265,35 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "The job murn_job was saved and received the ID: 4671\n",
- "The job strain_0_9 was saved and received the ID: 4672\n",
- "The job strain_0_92 was saved and received the ID: 4673\n",
- "The job strain_0_94 was saved and received the ID: 4674\n",
- "The job strain_0_96 was saved and received the ID: 4675\n",
- "The job strain_0_98 was saved and received the ID: 4676\n",
- "The job strain_1_0 was saved and received the ID: 4677\n",
- "The job strain_1_02 was saved and received the ID: 4678\n",
- "The job strain_1_04 was saved and received the ID: 4679\n",
- "The job strain_1_06 was saved and received the ID: 4680\n",
- "The job strain_1_08 was saved and received the ID: 4681\n",
- "The job strain_1_1 was saved and received the ID: 4682\n",
- "job_id: 4672 finished\n",
- "job_id: 4673 finished\n",
- "job_id: 4674 finished\n",
- "job_id: 4675 finished\n",
- "job_id: 4676 finished\n",
- "job_id: 4677 finished\n",
- "job_id: 4678 finished\n",
- "job_id: 4679 finished\n",
- "job_id: 4680 finished\n",
- "job_id: 4681 finished\n",
- "job_id: 4682 finished\n",
+ "The job murn_job was saved and received the ID: 126\n",
+ "The job strain_0_9 was saved and received the ID: 127\n",
+ "The job strain_0_92 was saved and received the ID: 128\n",
+ "The job strain_0_94 was saved and received the ID: 129\n",
+ "The job strain_0_96 was saved and received the ID: 130\n",
+ "The job strain_0_98 was saved and received the ID: 131\n",
+ "The job strain_1_0 was saved and received the ID: 132\n",
+ "The job strain_1_02 was saved and received the ID: 133\n",
+ "The job strain_1_04 was saved and received the ID: 134\n",
+ "The job strain_1_06 was saved and received the ID: 135\n",
+ "The job strain_1_08 was saved and received the ID: 136\n",
+ "The job strain_1_1 was saved and received the ID: 137\n",
+ "job_id: 127 finished\n",
+ "job_id: 128 finished\n",
+ "job_id: 129 finished\n",
+ "job_id: 130 finished\n",
+ "job_id: 131 finished\n",
+ "job_id: 132 finished\n",
+ "job_id: 133 finished\n",
+ "job_id: 134 finished\n",
+ "job_id: 135 finished\n",
+ "job_id: 136 finished\n",
+ "job_id: 137 finished\n",
"Potential: Cu-runner-df4\n"
]
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -223,35 +307,35 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "The job murn_job was saved and received the ID: 4683\n",
- "The job strain_0_9 was saved and received the ID: 4684\n",
- "The job strain_0_92 was saved and received the ID: 4685\n",
- "The job strain_0_94 was saved and received the ID: 4686\n",
- "The job strain_0_96 was saved and received the ID: 4687\n",
- "The job strain_0_98 was saved and received the ID: 4688\n",
- "The job strain_1_0 was saved and received the ID: 4689\n",
- "The job strain_1_02 was saved and received the ID: 4690\n",
- "The job strain_1_04 was saved and received the ID: 4691\n",
- "The job strain_1_06 was saved and received the ID: 4692\n",
- "The job strain_1_08 was saved and received the ID: 4693\n",
- "The job strain_1_1 was saved and received the ID: 4694\n",
- "job_id: 4684 finished\n",
- "job_id: 4685 finished\n",
- "job_id: 4686 finished\n",
- "job_id: 4687 finished\n",
- "job_id: 4688 finished\n",
- "job_id: 4689 finished\n",
- "job_id: 4690 finished\n",
- "job_id: 4691 finished\n",
- "job_id: 4692 finished\n",
- "job_id: 4693 finished\n",
- "job_id: 4694 finished\n",
+ "The job murn_job was saved and received the ID: 138\n",
+ "The job strain_0_9 was saved and received the ID: 139\n",
+ "The job strain_0_92 was saved and received the ID: 140\n",
+ "The job strain_0_94 was saved and received the ID: 141\n",
+ "The job strain_0_96 was saved and received the ID: 142\n",
+ "The job strain_0_98 was saved and received the ID: 143\n",
+ "The job strain_1_0 was saved and received the ID: 144\n",
+ "The job strain_1_02 was saved and received the ID: 145\n",
+ "The job strain_1_04 was saved and received the ID: 146\n",
+ "The job strain_1_06 was saved and received the ID: 147\n",
+ "The job strain_1_08 was saved and received the ID: 148\n",
+ "The job strain_1_1 was saved and received the ID: 149\n",
+ "job_id: 139 finished\n",
+ "job_id: 140 finished\n",
+ "job_id: 141 finished\n",
+ "job_id: 142 finished\n",
+ "job_id: 143 finished\n",
+ "job_id: 144 finished\n",
+ "job_id: 145 finished\n",
+ "job_id: 146 finished\n",
+ "job_id: 147 finished\n",
+ "job_id: 148 finished\n",
+ "job_id: 149 finished\n",
"Potential: Cu-atomicrex-df1-107-25\n"
]
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -279,13 +363,13 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "domestic-reminder",
+ "id": "independent-tennis",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "-3.716480589398982"
+ "-3.716480589399012"
]
},
"execution_count": 8,
@@ -300,7 +384,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "id": "swedish-vertical",
+ "id": "occupational-cross",
"metadata": {},
"outputs": [],
"source": [
@@ -332,29 +416,29 @@
{
"cell_type": "code",
"execution_count": 10,
- "id": "parliamentary-bronze",
+ "id": "collective-parameter",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 6/6 [00:00<00:00, 327.22it/s]\n",
- " 0%| | 0/6 [00:00<?, ?it/s]"
+ "100%|██████████| 5/5 [00:00<00:00, 997.41it/s]\n",
+ "100%|██████████| 5/5 [00:00<00:00, 43.30it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job table_murn was saved and received the ID: 4695\n"
+ "The job table_murn was saved and received the ID: 150\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 6/6 [00:00<00:00, 18.03it/s]\n"
+ "\n"
]
},
{
@@ -390,37 +474,27 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>4527</td>\n",
- " <td>df1_cut5_pyace</td>\n",
- " <td>3.626898</td>\n",
- " <td>11.927402</td>\n",
- " <td>152.904645</td>\n",
- " <td>-3.699069</td>\n",
+ " <td>90</td>\n",
+ " <td>2012--Mendelev-M-I--Cu--LAMMPS--ipr1</td>\n",
+ " <td>3.637410</td>\n",
+ " <td>12.031420</td>\n",
+ " <td>151.879230</td>\n",
+ " <td>-3.422980</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>4539</td>\n",
- " <td>RuNNer-Cu</td>\n",
- " <td>3.609436</td>\n",
- " <td>11.755954</td>\n",
- " <td>181.669440</td>\n",
- " <td>-3.693047</td>\n",
+ " <td>102</td>\n",
+ " <td>2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2</td>\n",
+ " <td>3.614942</td>\n",
+ " <td>11.809844</td>\n",
+ " <td>135.774799</td>\n",
+ " <td>-3.539942</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>4551</td>\n",
- " <td>EAM</td>\n",
- " <td>3.629413</td>\n",
- " <td>11.952236</td>\n",
- " <td>145.145002</td>\n",
- " <td>-3.696517</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4659</td>\n",
+ " <td>114</td>\n",
" <td>Cu-ace</td>\n",
" <td>3.629863</td>\n",
" <td>11.956678</td>\n",
@@ -429,8 +503,8 @@
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>4</th>\n",
- " <td>4671</td>\n",
+ " <th>3</th>\n",
+ " <td>126</td>\n",
" <td>Cu-runner-df4</td>\n",
" <td>3.618770</td>\n",
" <td>11.847397</td>\n",
@@ -439,8 +513,8 @@
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>5</th>\n",
- " <td>4683</td>\n",
+ " <th>4</th>\n",
+ " <td>138</td>\n",
" <td>Cu-atomicrex-df1-107-25</td>\n",
" <td>3.659051</td>\n",
" <td>12.247445</td>\n",
@@ -453,21 +527,19 @@
"</div>"
],
"text/plain": [
- " job_id potential a eq_vol eq_bm \\\n",
- "0 4527 df1_cut5_pyace 3.626898 11.927402 152.904645 \n",
- "1 4539 RuNNer-Cu 3.609436 11.755954 181.669440 \n",
- "2 4551 EAM 3.629413 11.952236 145.145002 \n",
- "3 4659 Cu-ace 3.629863 11.956678 146.220099 \n",
- "4 4671 Cu-runner-df4 3.618770 11.847397 145.857176 \n",
- "5 4683 Cu-atomicrex-df1-107-25 3.659051 12.247445 156.948283 \n",
+ " job_id potential a eq_vol \\\n",
+ "0 90 2012--Mendelev-M-I--Cu--LAMMPS--ipr1 3.637410 12.031420 \n",
+ "1 102 2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2 3.614942 11.809844 \n",
+ "2 114 Cu-ace 3.629863 11.956678 \n",
+ "3 126 Cu-runner-df4 3.618770 11.847397 \n",
+ "4 138 Cu-atomicrex-df1-107-25 3.659051 12.247445 \n",
"\n",
- " eq_energy n_atoms \n",
- "0 -3.699069 1 \n",
- "1 -3.693047 1 \n",
- "2 -3.696517 1 \n",
- "3 -3.698781 1 \n",
- "4 -3.694889 1 \n",
- "5 -3.716481 1 "
+ " eq_bm eq_energy n_atoms \n",
+ "0 151.879230 -3.422980 1 \n",
+ "1 135.774799 -3.539942 1 \n",
+ "2 146.220099 -3.698781 1 \n",
+ "3 145.857176 -3.694889 1 \n",
+ "4 156.948283 -3.716481 1 "
]
},
"execution_count": 10,
@@ -492,7 +564,7 @@
},
{
"cell_type": "markdown",
- "id": "brutal-smell",
+ "id": "valid-apollo",
"metadata": {},
"source": [
"## **Compute elastic constants**\n",
@@ -503,103 +575,88 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "intimate-mouse",
+ "id": "industrial-antique",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "df1_cut5_pyace\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2021-03-09 01:01:51,064 - pyiron_log - WARNING - The job elastic_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:01:51,064 - pyiron_log - WARNING - The job elastic_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "RuNNer-Cu\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2021-03-09 01:01:52,907 - pyiron_log - WARNING - The job elastic_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:01:52,907 - pyiron_log - WARNING - The job elastic_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "EAM\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2021-03-09 01:02:34,375 - pyiron_log - WARNING - The job elastic_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:02:34,375 - pyiron_log - WARNING - The job elastic_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "2012--Mendelev-M-I--Cu--LAMMPS--ipr1\n",
+ "The job elastic_job was saved and received the ID: 151\n",
+ "The job s_e_0 was saved and received the ID: 152\n",
+ "The job s_01_e_m0_05000 was saved and received the ID: 153\n",
+ "The job s_01_e_m0_02500 was saved and received the ID: 154\n",
+ "The job s_01_e_0_02500 was saved and received the ID: 155\n",
+ "The job s_01_e_0_05000 was saved and received the ID: 156\n",
+ "The job s_08_e_m0_05000 was saved and received the ID: 157\n",
+ "The job s_08_e_m0_02500 was saved and received the ID: 158\n",
+ "The job s_08_e_0_02500 was saved and received the ID: 159\n",
+ "The job s_08_e_0_05000 was saved and received the ID: 160\n",
+ "The job s_23_e_m0_05000 was saved and received the ID: 161\n",
+ "The job s_23_e_m0_02500 was saved and received the ID: 162\n",
+ "The job s_23_e_0_02500 was saved and received the ID: 163\n",
+ "The job s_23_e_0_05000 was saved and received the ID: 164\n",
+ "2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2\n",
+ "The job elastic_job was saved and received the ID: 165\n",
+ "The job s_e_0 was saved and received the ID: 166\n",
+ "The job s_01_e_m0_05000 was saved and received the ID: 167\n",
+ "The job s_01_e_m0_02500 was saved and received the ID: 168\n",
+ "The job s_01_e_0_02500 was saved and received the ID: 169\n",
+ "The job s_01_e_0_05000 was saved and received the ID: 170\n",
+ "The job s_08_e_m0_05000 was saved and received the ID: 171\n",
+ "The job s_08_e_m0_02500 was saved and received the ID: 172\n",
+ "The job s_08_e_0_02500 was saved and received the ID: 173\n",
+ "The job s_08_e_0_05000 was saved and received the ID: 174\n",
+ "The job s_23_e_m0_05000 was saved and received the ID: 175\n",
+ "The job s_23_e_m0_02500 was saved and received the ID: 176\n",
+ "The job s_23_e_0_02500 was saved and received the ID: 177\n",
+ "The job s_23_e_0_05000 was saved and received the ID: 178\n",
"Cu-ace\n",
- "The job elastic_job was saved and received the ID: 4696\n",
- "The job s_e_0 was saved and received the ID: 4697\n",
- "The job s_01_e_m0_05000 was saved and received the ID: 4698\n",
- "The job s_01_e_m0_02500 was saved and received the ID: 4699\n",
- "The job s_01_e_0_02500 was saved and received the ID: 4700\n",
- "The job s_01_e_0_05000 was saved and received the ID: 4701\n",
- "The job s_08_e_m0_05000 was saved and received the ID: 4702\n",
- "The job s_08_e_m0_02500 was saved and received the ID: 4703\n",
- "The job s_08_e_0_02500 was saved and received the ID: 4704\n",
- "The job s_08_e_0_05000 was saved and received the ID: 4705\n",
- "The job s_23_e_m0_05000 was saved and received the ID: 4706\n",
- "The job s_23_e_m0_02500 was saved and received the ID: 4707\n",
- "The job s_23_e_0_02500 was saved and received the ID: 4708\n",
- "The job s_23_e_0_05000 was saved and received the ID: 4709\n",
+ "The job elastic_job was saved and received the ID: 179\n",
+ "The job s_e_0 was saved and received the ID: 180\n",
+ "The job s_01_e_m0_05000 was saved and received the ID: 181\n",
+ "The job s_01_e_m0_02500 was saved and received the ID: 182\n",
+ "The job s_01_e_0_02500 was saved and received the ID: 183\n",
+ "The job s_01_e_0_05000 was saved and received the ID: 184\n",
+ "The job s_08_e_m0_05000 was saved and received the ID: 185\n",
+ "The job s_08_e_m0_02500 was saved and received the ID: 186\n",
+ "The job s_08_e_0_02500 was saved and received the ID: 187\n",
+ "The job s_08_e_0_05000 was saved and received the ID: 188\n",
+ "The job s_23_e_m0_05000 was saved and received the ID: 189\n",
+ "The job s_23_e_m0_02500 was saved and received the ID: 190\n",
+ "The job s_23_e_0_02500 was saved and received the ID: 191\n",
+ "The job s_23_e_0_05000 was saved and received the ID: 192\n",
"Cu-runner-df4\n",
- "The job elastic_job was saved and received the ID: 4710\n",
- "The job s_e_0 was saved and received the ID: 4711\n",
- "The job s_01_e_m0_05000 was saved and received the ID: 4712\n",
- "The job s_01_e_m0_02500 was saved and received the ID: 4713\n",
- "The job s_01_e_0_02500 was saved and received the ID: 4714\n",
- "The job s_01_e_0_05000 was saved and received the ID: 4715\n",
- "The job s_08_e_m0_05000 was saved and received the ID: 4716\n",
- "The job s_08_e_m0_02500 was saved and received the ID: 4717\n",
- "The job s_08_e_0_02500 was saved and received the ID: 4718\n",
- "The job s_08_e_0_05000 was saved and received the ID: 4719\n",
- "The job s_23_e_m0_05000 was saved and received the ID: 4720\n",
- "The job s_23_e_m0_02500 was saved and received the ID: 4721\n",
- "The job s_23_e_0_02500 was saved and received the ID: 4722\n",
- "The job s_23_e_0_05000 was saved and received the ID: 4723\n",
+ "The job elastic_job was saved and received the ID: 193\n",
+ "The job s_e_0 was saved and received the ID: 194\n",
+ "The job s_01_e_m0_05000 was saved and received the ID: 195\n",
+ "The job s_01_e_m0_02500 was saved and received the ID: 196\n",
+ "The job s_01_e_0_02500 was saved and received the ID: 197\n",
+ "The job s_01_e_0_05000 was saved and received the ID: 198\n",
+ "The job s_08_e_m0_05000 was saved and received the ID: 199\n",
+ "The job s_08_e_m0_02500 was saved and received the ID: 200\n",
+ "The job s_08_e_0_02500 was saved and received the ID: 201\n",
+ "The job s_08_e_0_05000 was saved and received the ID: 202\n",
+ "The job s_23_e_m0_05000 was saved and received the ID: 203\n",
+ "The job s_23_e_m0_02500 was saved and received the ID: 204\n",
+ "The job s_23_e_0_02500 was saved and received the ID: 205\n",
+ "The job s_23_e_0_05000 was saved and received the ID: 206\n",
"Cu-atomicrex-df1-107-25\n",
- "The job elastic_job was saved and received the ID: 4724\n",
- "The job s_e_0 was saved and received the ID: 4725\n",
- "The job s_01_e_m0_05000 was saved and received the ID: 4726\n",
- "The job s_01_e_m0_02500 was saved and received the ID: 4727\n",
- "The job s_01_e_0_02500 was saved and received the ID: 4728\n",
- "The job s_01_e_0_05000 was saved and received the ID: 4729\n",
- "The job s_08_e_m0_05000 was saved and received the ID: 4730\n",
- "The job s_08_e_m0_02500 was saved and received the ID: 4731\n",
- "The job s_08_e_0_02500 was saved and received the ID: 4732\n",
- "The job s_08_e_0_05000 was saved and received the ID: 4733\n",
- "The job s_23_e_m0_05000 was saved and received the ID: 4734\n",
- "The job s_23_e_m0_02500 was saved and received the ID: 4735\n",
- "The job s_23_e_0_02500 was saved and received the ID: 4736\n",
- "The job s_23_e_0_05000 was saved and received the ID: 4737\n"
+ "The job elastic_job was saved and received the ID: 207\n",
+ "The job s_e_0 was saved and received the ID: 208\n",
+ "The job s_01_e_m0_05000 was saved and received the ID: 209\n",
+ "The job s_01_e_m0_02500 was saved and received the ID: 210\n",
+ "The job s_01_e_0_02500 was saved and received the ID: 211\n",
+ "The job s_01_e_0_05000 was saved and received the ID: 212\n",
+ "The job s_08_e_m0_05000 was saved and received the ID: 213\n",
+ "The job s_08_e_m0_02500 was saved and received the ID: 214\n",
+ "The job s_08_e_0_02500 was saved and received the ID: 215\n",
+ "The job s_08_e_0_05000 was saved and received the ID: 216\n",
+ "The job s_23_e_m0_05000 was saved and received the ID: 217\n",
+ "The job s_23_e_m0_02500 was saved and received the ID: 218\n",
+ "The job s_23_e_0_02500 was saved and received the ID: 219\n",
+ "The job s_23_e_0_05000 was saved and received the ID: 220\n"
]
}
],
@@ -621,7 +678,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "id": "abstract-treasure",
+ "id": "under-knight",
"metadata": {},
"outputs": [
{
@@ -645,7 +702,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "juvenile-secondary",
+ "id": "removable-clinic",
"metadata": {},
"outputs": [],
"source": [
@@ -669,29 +726,29 @@
{
"cell_type": "code",
"execution_count": 14,
- "id": "silent-queensland",
+ "id": "ongoing-chocolate",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 6/6 [00:00<00:00, 438.64it/s]\n",
- " 0%| | 0/6 [00:00<?, ?it/s]"
+ "100%|██████████| 5/5 [00:00<00:00, 993.02it/s]\n",
+ " 20%|██ | 1/5 [00:00<00:00, 7.41it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job table_elastic was saved and received the ID: 4738\n"
+ "The job table_elastic was saved and received the ID: 221\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 6/6 [00:01<00:00, 3.40it/s]\n"
+ "100%|██████████| 5/5 [00:00<00:00, 7.61it/s]\n"
]
},
{
@@ -725,47 +782,39 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>4564</td>\n",
- " <td>df1_cut5_pyace</td>\n",
- " <td>194.440496</td>\n",
- " <td>128.345526</td>\n",
- " <td>89.439085</td>\n",
+ " <td>151</td>\n",
+ " <td>2012--Mendelev-M-I--Cu--LAMMPS--ipr1</td>\n",
+ " <td>178.311163</td>\n",
+ " <td>157.392048</td>\n",
+ " <td>89.559784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>4578</td>\n",
- " <td>RuNNer-Cu</td>\n",
- " <td>285.914838</td>\n",
- " <td>100.579897</td>\n",
- " <td>94.756733</td>\n",
+ " <td>165</td>\n",
+ " <td>2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2</td>\n",
+ " <td>168.530421</td>\n",
+ " <td>122.591406</td>\n",
+ " <td>84.710381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>4592</td>\n",
- " <td>EAM</td>\n",
- " <td>186.747051</td>\n",
- " <td>120.391617</td>\n",
- " <td>78.608540</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4696</td>\n",
+ " <td>179</td>\n",
" <td>Cu-ace</td>\n",
" <td>182.054447</td>\n",
" <td>132.575877</td>\n",
" <td>81.049351</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>4</th>\n",
- " <td>4710</td>\n",
+ " <th>3</th>\n",
+ " <td>193</td>\n",
" <td>Cu-runner-df4</td>\n",
" <td>143.473893</td>\n",
" <td>170.027784</td>\n",
" <td>100.953889</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>5</th>\n",
- " <td>4724</td>\n",
+ " <th>4</th>\n",
+ " <td>207</td>\n",
" <td>Cu-atomicrex-df1-107-25</td>\n",
" <td>201.538410</td>\n",
" <td>131.169225</td>\n",
@@ -776,13 +825,19 @@
"</div>"
],
"text/plain": [
- " job_id potential C11 C12 C44\n",
- "0 4564 df1_cut5_pyace 194.440496 128.345526 89.439085\n",
- "1 4578 RuNNer-Cu 285.914838 100.579897 94.756733\n",
- "2 4592 EAM 186.747051 120.391617 78.608540\n",
- "3 4696 Cu-ace 182.054447 132.575877 81.049351\n",
- "4 4710 Cu-runner-df4 143.473893 170.027784 100.953889\n",
- "5 4724 Cu-atomicrex-df1-107-25 201.538410 131.169225 70.714601"
+ " job_id potential C11 C12 \\\n",
+ "0 151 2012--Mendelev-M-I--Cu--LAMMPS--ipr1 178.311163 157.392048 \n",
+ "1 165 2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2 168.530421 122.591406 \n",
+ "2 179 Cu-ace 182.054447 132.575877 \n",
+ "3 193 Cu-runner-df4 143.473893 170.027784 \n",
+ "4 207 Cu-atomicrex-df1-107-25 201.538410 131.169225 \n",
+ "\n",
+ " C44 \n",
+ "0 89.559784 \n",
+ "1 84.710381 \n",
+ "2 81.049351 \n",
+ "3 100.953889 \n",
+ "4 70.714601 "
]
},
"execution_count": 14,
@@ -805,7 +860,7 @@
},
{
"cell_type": "markdown",
- "id": "genuine-princess",
+ "id": "considerable-latex",
"metadata": {},
"source": [
"## **Compare computed elastic constants with that from DFT calculations**\n",
@@ -816,7 +871,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "positive-exclusion",
+ "id": "governmental-watershed",
"metadata": {},
"outputs": [
{
@@ -824,13 +879,13 @@
"output_type": "stream",
"text": [
"Querying...done\n",
- "Response successful, size = 7.20 kB, time = 0.13 s\n",
+ "Response successful, size = 7.20 kB, time = 0.04 s\n",
"1 entries received\n",
"Querying...done\n",
- "Response successful, size = 37.78 kB, time = 0.35 s\n",
+ "Response successful, size = 37.78 kB, time = 0.36 s\n",
"1 entries received\n",
"Querying...done\n",
- "Response successful, size = 22.50 kB, time = 0.21 s\n",
+ "Response successful, size = 22.50 kB, time = 1.13 s\n",
"1 entries received\n"
]
}
@@ -856,7 +911,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "id": "asian-employment",
+ "id": "soviet-restaurant",
"metadata": {},
"outputs": [
{
@@ -878,7 +933,7 @@
},
{
"cell_type": "markdown",
- "id": "rubber-estate",
+ "id": "cardiovascular-annex",
"metadata": {},
"source": [
"## **Calculation of surface energies**\n",
@@ -889,46 +944,28 @@
{
"cell_type": "code",
"execution_count": 17,
- "id": "entire-trout",
+ "id": "individual-samuel",
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2021-03-09 01:04:52,270 - pyiron_log - WARNING - The job surf_fcc111 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:04:52,270 - pyiron_log - WARNING - The job surf_fcc111 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:04:54,228 - pyiron_log - WARNING - The job surf_fcc110 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:04:54,228 - pyiron_log - WARNING - The job surf_fcc110 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:04:56,439 - pyiron_log - WARNING - The job surf_fcc100 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:04:56,439 - pyiron_log - WARNING - The job surf_fcc100 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:04:57,383 - pyiron_log - WARNING - Job aborted - please remove it and run again! surf_fcc111\n",
- "2021-03-09 01:04:57,383 - pyiron_log - WARNING - Job aborted - please remove it and run again! surf_fcc111\n",
- "2021-03-09 01:04:58,456 - pyiron_log - WARNING - Job aborted - please remove it and run again! surf_fcc110\n",
- "2021-03-09 01:04:58,456 - pyiron_log - WARNING - Job aborted - please remove it and run again! surf_fcc110\n",
- "2021-03-09 01:04:59,514 - pyiron_log - WARNING - Job aborted - please remove it and run again! surf_fcc100\n",
- "2021-03-09 01:04:59,514 - pyiron_log - WARNING - Job aborted - please remove it and run again! surf_fcc100\n",
- "2021-03-09 01:05:34,428 - pyiron_log - WARNING - The job surf_fcc111 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:05:34,428 - pyiron_log - WARNING - The job surf_fcc111 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:06:12,553 - pyiron_log - WARNING - The job surf_fcc110 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:06:12,553 - pyiron_log - WARNING - The job surf_fcc110 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:06:56,630 - pyiron_log - WARNING - The job surf_fcc100 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:06:56,630 - pyiron_log - WARNING - The job surf_fcc100 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job surf_fcc111 was saved and received the ID: 4739\n",
- "The job surf_fcc110 was saved and received the ID: 4740\n",
- "The job surf_fcc100 was saved and received the ID: 4741\n",
- "The job surf_fcc111 was saved and received the ID: 4742\n",
- "The job surf_fcc110 was saved and received the ID: 4743\n",
- "The job surf_fcc100 was saved and received the ID: 4744\n",
- "The job surf_fcc111 was saved and received the ID: 4745\n",
- "The job surf_fcc110 was saved and received the ID: 4746\n",
- "The job surf_fcc100 was saved and received the ID: 4747\n"
+ "The job surf_fcc111 was saved and received the ID: 222\n",
+ "The job surf_fcc110 was saved and received the ID: 223\n",
+ "The job surf_fcc100 was saved and received the ID: 224\n",
+ "The job surf_fcc111 was saved and received the ID: 225\n",
+ "The job surf_fcc110 was saved and received the ID: 226\n",
+ "The job surf_fcc100 was saved and received the ID: 227\n",
+ "The job surf_fcc111 was saved and received the ID: 228\n",
+ "The job surf_fcc110 was saved and received the ID: 229\n",
+ "The job surf_fcc100 was saved and received the ID: 230\n",
+ "The job surf_fcc111 was saved and received the ID: 231\n",
+ "The job surf_fcc110 was saved and received the ID: 232\n",
+ "The job surf_fcc100 was saved and received the ID: 233\n",
+ "The job surf_fcc111 was saved and received the ID: 234\n",
+ "The job surf_fcc110 was saved and received the ID: 235\n",
+ "The job surf_fcc100 was saved and received the ID: 236\n"
]
}
],
@@ -952,7 +989,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "native-breach",
+ "id": "applicable-tunnel",
"metadata": {},
"outputs": [],
"source": [
@@ -976,29 +1013,29 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "tracked-renaissance",
+ "id": "false-jacob",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 15/15 [00:00<00:00, 595.30it/s]\n",
- " 0%| | 0/15 [00:00<?, ?it/s]"
+ "100%|██████████| 15/15 [00:00<00:00, 1154.69it/s]\n",
+ " 60%|██████ | 9/15 [00:00<00:00, 80.53it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job table_surface was saved and received the ID: 4748\n"
+ "The job table_surface was saved and received the ID: 237\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 15/15 [00:00<00:00, 30.63it/s]\n"
+ "100%|██████████| 15/15 [00:00<00:00, 79.62it/s]\n"
]
},
{
@@ -1034,61 +1071,61 @@
" <tr>\n",
" <th>0</th>\n",
" <td>512</td>\n",
- " <td>4607</td>\n",
- " <td>-1810.173247</td>\n",
- " <td>df1_cut5_pyace</td>\n",
+ " <td>222</td>\n",
+ " <td>-1705.746713</td>\n",
+ " <td>2012--Mendelev-M-I--Cu--LAMMPS--ipr1</td>\n",
" <td>fcc111</td>\n",
- " <td>364.545025</td>\n",
+ " <td>366.661398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>512</td>\n",
- " <td>4608</td>\n",
- " <td>-1747.136068</td>\n",
- " <td>df1_cut5_pyace</td>\n",
+ " <td>223</td>\n",
+ " <td>-1657.007374</td>\n",
+ " <td>2012--Mendelev-M-I--Cu--LAMMPS--ipr1</td>\n",
" <td>fcc110</td>\n",
- " <td>595.299533</td>\n",
+ " <td>598.755556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>512</td>\n",
- " <td>4609</td>\n",
- " <td>-1798.055813</td>\n",
- " <td>df1_cut5_pyace</td>\n",
+ " <td>224</td>\n",
+ " <td>-1689.102779</td>\n",
+ " <td>2012--Mendelev-M-I--Cu--LAMMPS--ipr1</td>\n",
" <td>fcc100</td>\n",
- " <td>420.940337</td>\n",
+ " <td>423.384114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>512</td>\n",
- " <td>4613</td>\n",
- " <td>-1818.524364</td>\n",
- " <td>EAM</td>\n",
+ " <td>225</td>\n",
+ " <td>-1744.607843</td>\n",
+ " <td>2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2</td>\n",
" <td>fcc111</td>\n",
- " <td>365.050871</td>\n",
+ " <td>362.145737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>512</td>\n",
- " <td>4614</td>\n",
- " <td>-1751.340966</td>\n",
- " <td>EAM</td>\n",
+ " <td>226</td>\n",
+ " <td>-1683.836637</td>\n",
+ " <td>2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2</td>\n",
" <td>fcc110</td>\n",
- " <td>596.125576</td>\n",
+ " <td>591.381512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>512</td>\n",
- " <td>4615</td>\n",
- " <td>-1804.257097</td>\n",
- " <td>EAM</td>\n",
+ " <td>227</td>\n",
+ " <td>-1730.881227</td>\n",
+ " <td>2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2</td>\n",
" <td>fcc100</td>\n",
- " <td>421.524437</td>\n",
+ " <td>418.169878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>512</td>\n",
- " <td>4739</td>\n",
+ " <td>228</td>\n",
" <td>-1834.286336</td>\n",
" <td>Cu-ace</td>\n",
" <td>fcc111</td>\n",
@@ -1097,7 +1134,7 @@
" <tr>\n",
" <th>7</th>\n",
" <td>512</td>\n",
- " <td>4740</td>\n",
+ " <td>229</td>\n",
" <td>-1775.620101</td>\n",
" <td>Cu-ace</td>\n",
" <td>fcc110</td>\n",
@@ -1106,7 +1143,7 @@
" <tr>\n",
" <th>8</th>\n",
" <td>512</td>\n",
- " <td>4741</td>\n",
+ " <td>230</td>\n",
" <td>-1814.469585</td>\n",
" <td>Cu-ace</td>\n",
" <td>fcc100</td>\n",
@@ -1115,7 +1152,7 @@
" <tr>\n",
" <th>9</th>\n",
" <td>512</td>\n",
- " <td>4742</td>\n",
+ " <td>231</td>\n",
" <td>-1835.853699</td>\n",
" <td>Cu-runner-df4</td>\n",
" <td>fcc111</td>\n",
@@ -1124,7 +1161,7 @@
" <tr>\n",
" <th>10</th>\n",
" <td>512</td>\n",
- " <td>4743</td>\n",
+ " <td>232</td>\n",
" <td>-1786.742960</td>\n",
" <td>Cu-runner-df4</td>\n",
" <td>fcc110</td>\n",
@@ -1133,7 +1170,7 @@
" <tr>\n",
" <th>11</th>\n",
" <td>512</td>\n",
- " <td>4744</td>\n",
+ " <td>233</td>\n",
" <td>-1816.715931</td>\n",
" <td>Cu-runner-df4</td>\n",
" <td>fcc100</td>\n",
@@ -1142,7 +1179,7 @@
" <tr>\n",
" <th>12</th>\n",
" <td>512</td>\n",
- " <td>4745</td>\n",
+ " <td>234</td>\n",
" <td>-1830.588920</td>\n",
" <td>Cu-atomicrex-df1-107-25</td>\n",
" <td>fcc111</td>\n",
@@ -1151,7 +1188,7 @@
" <tr>\n",
" <th>13</th>\n",
" <td>512</td>\n",
- " <td>4746</td>\n",
+ " <td>235</td>\n",
" <td>-1765.956124</td>\n",
" <td>Cu-atomicrex-df1-107-25</td>\n",
" <td>fcc110</td>\n",
@@ -1160,7 +1197,7 @@
" <tr>\n",
" <th>14</th>\n",
" <td>512</td>\n",
- " <td>4747</td>\n",
+ " <td>236</td>\n",
" <td>-1812.502127</td>\n",
" <td>Cu-atomicrex-df1-107-25</td>\n",
" <td>fcc100</td>\n",
@@ -1171,39 +1208,39 @@
"</div>"
],
"text/plain": [
- " Number_of_atoms job_id energy_tot potential \\\n",
- "0 512 4607 -1810.173247 df1_cut5_pyace \n",
- "1 512 4608 -1747.136068 df1_cut5_pyace \n",
- "2 512 4609 -1798.055813 df1_cut5_pyace \n",
- "3 512 4613 -1818.524364 EAM \n",
- "4 512 4614 -1751.340966 EAM \n",
- "5 512 4615 -1804.257097 EAM \n",
- "6 512 4739 -1834.286336 Cu-ace \n",
- "7 512 4740 -1775.620101 Cu-ace \n",
- "8 512 4741 -1814.469585 Cu-ace \n",
- "9 512 4742 -1835.853699 Cu-runner-df4 \n",
- "10 512 4743 -1786.742960 Cu-runner-df4 \n",
- "11 512 4744 -1816.715931 Cu-runner-df4 \n",
- "12 512 4745 -1830.588920 Cu-atomicrex-df1-107-25 \n",
- "13 512 4746 -1765.956124 Cu-atomicrex-df1-107-25 \n",
- "14 512 4747 -1812.502127 Cu-atomicrex-df1-107-25 \n",
+ " Number_of_atoms job_id energy_tot \\\n",
+ "0 512 222 -1705.746713 \n",
+ "1 512 223 -1657.007374 \n",
+ "2 512 224 -1689.102779 \n",
+ "3 512 225 -1744.607843 \n",
+ "4 512 226 -1683.836637 \n",
+ "5 512 227 -1730.881227 \n",
+ "6 512 228 -1834.286336 \n",
+ "7 512 229 -1775.620101 \n",
+ "8 512 230 -1814.469585 \n",
+ "9 512 231 -1835.853699 \n",
+ "10 512 232 -1786.742960 \n",
+ "11 512 233 -1816.715931 \n",
+ "12 512 234 -1830.588920 \n",
+ "13 512 235 -1765.956124 \n",
+ "14 512 236 -1812.502127 \n",
"\n",
- " surface_type surface_area \n",
- "0 fcc111 364.545025 \n",
- "1 fcc110 595.299533 \n",
- "2 fcc100 420.940337 \n",
- "3 fcc111 365.050871 \n",
- "4 fcc110 596.125576 \n",
- "5 fcc100 421.524437 \n",
- "6 fcc111 365.141308 \n",
- "7 fcc110 596.273259 \n",
- "8 fcc100 421.628865 \n",
- "9 fcc111 362.913030 \n",
- "10 fcc110 592.634496 \n",
- "11 fcc100 419.055871 \n",
- "12 fcc111 371.037336 \n",
- "13 fcc110 605.901433 \n",
- "14 fcc100 428.437012 "
+ " potential surface_type surface_area \n",
+ "0 2012--Mendelev-M-I--Cu--LAMMPS--ipr1 fcc111 366.661398 \n",
+ "1 2012--Mendelev-M-I--Cu--LAMMPS--ipr1 fcc110 598.755556 \n",
+ "2 2012--Mendelev-M-I--Cu--LAMMPS--ipr1 fcc100 423.384114 \n",
+ "3 2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2 fcc111 362.145737 \n",
+ "4 2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2 fcc110 591.381512 \n",
+ "5 2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2 fcc100 418.169878 \n",
+ "6 Cu-ace fcc111 365.141308 \n",
+ "7 Cu-ace fcc110 596.273259 \n",
+ "8 Cu-ace fcc100 421.628865 \n",
+ "9 Cu-runner-df4 fcc111 362.913030 \n",
+ "10 Cu-runner-df4 fcc110 592.634496 \n",
+ "11 Cu-runner-df4 fcc100 419.055871 \n",
+ "12 Cu-atomicrex-df1-107-25 fcc111 371.037336 \n",
+ "13 Cu-atomicrex-df1-107-25 fcc110 605.901433 \n",
+ "14 Cu-atomicrex-df1-107-25 fcc100 428.437012 "
]
},
"execution_count": 19,
@@ -1227,7 +1264,7 @@
},
{
"cell_type": "markdown",
- "id": "sunset-surface",
+ "id": "polished-catalog",
"metadata": {},
"source": [
"Compute surface energies using data from the bulk fcc crystal"
@@ -1236,7 +1273,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "suitable-wichita",
+ "id": "devoted-lover",
"metadata": {},
"outputs": [
{
@@ -1268,39 +1305,39 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>df1_cut5_pyace</td>\n",
+ " <td>2012--Mendelev-M-I--Cu--LAMMPS--ipr1</td>\n",
" <td>fcc111</td>\n",
- " <td>1840.428130</td>\n",
+ " <td>1022.919566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>df1_cut5_pyace</td>\n",
+ " <td>2012--Mendelev-M-I--Cu--LAMMPS--ipr1</td>\n",
" <td>fcc110</td>\n",
- " <td>1975.319222</td>\n",
+ " <td>1278.507591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>df1_cut5_pyace</td>\n",
+ " <td>2012--Mendelev-M-I--Cu--LAMMPS--ipr1</td>\n",
" <td>fcc100</td>\n",
- " <td>1824.465406</td>\n",
+ " <td>1200.798101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>EAM</td>\n",
+ " <td>2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2</td>\n",
" <td>fcc111</td>\n",
- " <td>1625.938342</td>\n",
+ " <td>1500.729057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>EAM</td>\n",
+ " <td>2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2</td>\n",
" <td>fcc110</td>\n",
- " <td>1898.514504</td>\n",
+ " <td>1742.221732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
- " <td>EAM</td>\n",
+ " <td>2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2</td>\n",
" <td>fcc100</td>\n",
- " <td>1679.249194</td>\n",
+ " <td>1562.632546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
@@ -1361,22 +1398,39 @@
"</div>"
],
"text/plain": [
- " potential surface_type surface_energy_in_mJ_per_sq_m\n",
- "0 df1_cut5_pyace fcc111 1840.428130\n",
- "1 df1_cut5_pyace fcc110 1975.319222\n",
- "2 df1_cut5_pyace fcc100 1824.465406\n",
- "3 EAM fcc111 1625.938342\n",
- "4 EAM fcc110 1898.514504\n",
- "5 EAM fcc100 1679.249194\n",
- "6 Cu-ace fcc111 1305.155196\n",
- "7 Cu-ace fcc110 1587.423774\n",
- "8 Cu-ace fcc100 1506.815901\n",
- "9 Cu-runner-df4 fcc111 1234.585883\n",
- "10 Cu-runner-df4 fcc110 1419.881876\n",
- "11 Cu-runner-df4 fcc100 1435.033014\n",
- "12 Cu-atomicrex-df1-107-25 fcc111 1559.908417\n",
- "13 Cu-atomicrex-df1-107-25 fcc110 1809.790013\n",
- "14 Cu-atomicrex-df1-107-25 fcc100 1689.108640"
+ " potential surface_type \\\n",
+ "0 2012--Mendelev-M-I--Cu--LAMMPS--ipr1 fcc111 \n",
+ "1 2012--Mendelev-M-I--Cu--LAMMPS--ipr1 fcc110 \n",
+ "2 2012--Mendelev-M-I--Cu--LAMMPS--ipr1 fcc100 \n",
+ "3 2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2 fcc111 \n",
+ "4 2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2 fcc110 \n",
+ "5 2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2 fcc100 \n",
+ "6 Cu-ace fcc111 \n",
+ "7 Cu-ace fcc110 \n",
+ "8 Cu-ace fcc100 \n",
+ "9 Cu-runner-df4 fcc111 \n",
+ "10 Cu-runner-df4 fcc110 \n",
+ "11 Cu-runner-df4 fcc100 \n",
+ "12 Cu-atomicrex-df1-107-25 fcc111 \n",
+ "13 Cu-atomicrex-df1-107-25 fcc110 \n",
+ "14 Cu-atomicrex-df1-107-25 fcc100 \n",
+ "\n",
+ " surface_energy_in_mJ_per_sq_m \n",
+ "0 1022.919566 \n",
+ "1 1278.507591 \n",
+ "2 1200.798101 \n",
+ "3 1500.729057 \n",
+ "4 1742.221732 \n",
+ "5 1562.632546 \n",
+ "6 1305.155196 \n",
+ "7 1587.423774 \n",
+ "8 1506.815901 \n",
+ "9 1234.585883 \n",
+ "10 1419.881876 \n",
+ "11 1435.033014 \n",
+ "12 1559.908417 \n",
+ "13 1809.790013 \n",
+ "14 1689.108640 "
]
},
"execution_count": 20,
@@ -1393,7 +1447,7 @@
},
{
"cell_type": "markdown",
- "id": "important-berkeley",
+ "id": "accredited-excuse",
"metadata": {},
"source": [
"## **Finite temperature thermodynamics (Harmonic approximation)**\n",
@@ -1404,31 +1458,23 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "cardiac-black",
+ "id": "seasonal-helmet",
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2021-03-09 01:08:09,341 - pyiron_log - WARNING - The job phonopy_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:08:09,341 - pyiron_log - WARNING - The job phonopy_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:08:11,367 - pyiron_log - WARNING - The job phonopy_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:08:11,367 - pyiron_log - WARNING - The job phonopy_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:09:03,325 - pyiron_log - WARNING - The job phonopy_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
- "2021-03-09 01:09:03,325 - pyiron_log - WARNING - The job phonopy_job is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job phonopy_job was saved and received the ID: 4749\n",
- "The job ref_job_0 was saved and received the ID: 4750\n",
- "The job phonopy_job was saved and received the ID: 4751\n",
- "The job ref_job_0 was saved and received the ID: 4752\n",
- "The job phonopy_job was saved and received the ID: 4753\n",
- "The job ref_job_0 was saved and received the ID: 4754\n"
+ "The job phonopy_job was saved and received the ID: 238\n",
+ "The job ref_job_0 was saved and received the ID: 239\n",
+ "The job phonopy_job was saved and received the ID: 240\n",
+ "The job ref_job_0 was saved and received the ID: 241\n",
+ "The job phonopy_job was saved and received the ID: 242\n",
+ "The job ref_job_0 was saved and received the ID: 243\n",
+ "The job phonopy_job was saved and received the ID: 244\n",
+ "The job ref_job_0 was saved and received the ID: 245\n",
+ "The job phonopy_job was saved and received the ID: 246\n",
+ "The job ref_job_0 was saved and received the ID: 247\n"
]
}
],
@@ -1447,7 +1493,7 @@
},
{
"cell_type": "markdown",
- "id": "comparative-concrete",
+ "id": "clean-kruger",
"metadata": {},
"source": [
"Plotting the thermodynamic properties using phonopy"
@@ -1456,12 +1502,12 @@
{
"cell_type": "code",
"execution_count": 22,
- "id": "least-trance",
+ "id": "mathematical-monitor",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1440x288 with 3 Axes>"
]
@@ -1507,7 +1553,7 @@
},
{
"cell_type": "markdown",
- "id": "impaired-reception",
+ "id": "eastern-dayton",
"metadata": {},
"source": [
"We can also compute the phonon density of states and compare with that from benchmarked DFT calculations"
@@ -1516,12 +1562,12 @@
{
"cell_type": "code",
"execution_count": 23,
- "id": "exclusive-prerequisite",
+ "id": "general-eating",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1546,7 +1592,7 @@
},
{
"cell_type": "markdown",
- "id": "stable-northwest",
+ "id": "flush-cleaning",
"metadata": {},
"source": [
"## **Validating against original dataset**\n",
@@ -1557,7 +1603,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "id": "champion-wright",
+ "id": "weekly-austria",
"metadata": {},
"outputs": [],
"source": [
@@ -1569,7 +1615,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "id": "exclusive-trick",
+ "id": "whole-syracuse",
"metadata": {},
"outputs": [
{
@@ -1613,13 +1659,13 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>4459</td>\n",
+ " <td>1</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df1_A1_A2_A3_EV_elast_phon</td>\n",
" <td>/df1_A1_A2_A3_EV_elast_phon</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
" <td>2021-02-18 19:49:53.061360</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
@@ -1631,13 +1677,13 @@
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>4460</td>\n",
+ " <td>2</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df3_10k</td>\n",
" <td>/df3_10k</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
" <td>2021-02-18 19:49:55.496691</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
@@ -1649,13 +1695,13 @@
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>4461</td>\n",
+ " <td>3</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df2_1k</td>\n",
" <td>/df2_1k</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
" <td>2021-02-18 19:49:56.101883</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
@@ -1667,13 +1713,13 @@
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>4462</td>\n",
+ " <td>4</td>\n",
" <td>finished</td>\n",
" <td>None</td>\n",
" <td>df4_2_5eV_25A3_8K</td>\n",
" <td>/df4_2_5eV_25A3_8K</td>\n",
- " <td>/home/surendralal/</td>\n",
- " <td>notebooks/pyiron_potentialfit/datasets/imported_datasets/Cu_database/</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>datasets/Cu_database/</td>\n",
" <td>2021-02-18 19:49:57.547918</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
@@ -1688,23 +1734,17 @@
"</div>"
],
"text/plain": [
- " id status chemicalformula job \\\n",
- "0 4459 finished None df1_A1_A2_A3_EV_elast_phon \n",
- "1 4460 finished None df3_10k \n",
- "2 4461 finished None df2_1k \n",
- "3 4462 finished None df4_2_5eV_25A3_8K \n",
- "\n",
- " subjob projectpath \\\n",
- "0 /df1_A1_A2_A3_EV_elast_phon /home/surendralal/ \n",
- "1 /df3_10k /home/surendralal/ \n",
- "2 /df2_1k /home/surendralal/ \n",
- "3 /df4_2_5eV_25A3_8K /home/surendralal/ \n",
+ " id status chemicalformula job \\\n",
+ "0 1 finished None df1_A1_A2_A3_EV_elast_phon \n",
+ "1 2 finished None df3_10k \n",
+ "2 3 finished None df2_1k \n",
+ "3 4 finished None df4_2_5eV_25A3_8K \n",
"\n",
- " project \\\n",
- "0 notebooks/pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
- "1 notebooks/pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
- "2 notebooks/pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
- "3 notebooks/pyiron_potentialfit/datasets/imported_datasets/Cu_database/ \n",
+ " subjob projectpath project \\\n",
+ "0 /df1_A1_A2_A3_EV_elast_phon /home/pyiron/ datasets/Cu_database/ \n",
+ "1 /df3_10k /home/pyiron/ datasets/Cu_database/ \n",
+ "2 /df2_1k /home/pyiron/ datasets/Cu_database/ \n",
+ "3 /df4_2_5eV_25A3_8K /home/pyiron/ datasets/Cu_database/ \n",
"\n",
" timestart timestop totalcputime computer \\\n",
"0 2021-02-18 19:49:53.061360 None None zora@cmti001#1 \n",
@@ -1731,7 +1771,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "id": "prostate-mistress",
+ "id": "alert-adjustment",
"metadata": {},
"outputs": [],
"source": [
@@ -1741,7 +1781,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "id": "shared-great",
+ "id": "center-contributor",
"metadata": {},
"outputs": [
{
@@ -1946,7 +1986,7 @@
},
{
"cell_type": "markdown",
- "id": "explicit-memorabilia",
+ "id": "empirical-ceiling",
"metadata": {},
"source": [
"Now we iterate over the structures in the dataset and compute the energies and forces for comparison"
@@ -1955,7 +1995,7 @@
{
"cell_type": "code",
"execution_count": 28,
- "id": "positive-scanner",
+ "id": "compliant-crazy",
"metadata": {},
"outputs": [],
"source": [
@@ -1967,49 +2007,73 @@
{
"cell_type": "code",
"execution_count": 29,
- "id": "worthy-couple",
+ "id": "negative-system",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The job lammps_struct_0 was saved and received the ID: 4755\n",
- "The job lammps_struct_1 was saved and received the ID: 4756\n",
- "The job lammps_struct_2 was saved and received the ID: 4757\n",
- "The job lammps_struct_3 was saved and received the ID: 4758\n",
- "The job lammps_struct_4 was saved and received the ID: 4759\n",
- "The job lammps_struct_5 was saved and received the ID: 4760\n",
- "The job lammps_struct_6 was saved and received the ID: 4761\n",
- "The job lammps_struct_7 was saved and received the ID: 4762\n",
- "The job lammps_struct_8 was saved and received the ID: 4763\n",
- "The job lammps_struct_9 was saved and received the ID: 4764\n",
- "The job lammps_struct_10 was saved and received the ID: 4765\n",
- "The job lammps_box was saved and received the ID: 4766\n",
- "The job lammps_struct_0 was saved and received the ID: 4767\n",
- "The job lammps_struct_1 was saved and received the ID: 4768\n",
- "The job lammps_struct_2 was saved and received the ID: 4769\n",
- "The job lammps_struct_3 was saved and received the ID: 4770\n",
- "The job lammps_struct_4 was saved and received the ID: 4771\n",
- "The job lammps_struct_5 was saved and received the ID: 4772\n",
- "The job lammps_struct_6 was saved and received the ID: 4773\n",
- "The job lammps_struct_7 was saved and received the ID: 4774\n",
- "The job lammps_struct_8 was saved and received the ID: 4775\n",
- "The job lammps_struct_9 was saved and received the ID: 4776\n",
- "The job lammps_struct_10 was saved and received the ID: 4777\n",
- "The job lammps_box was saved and received the ID: 4778\n",
- "The job lammps_struct_0 was saved and received the ID: 4779\n",
- "The job lammps_struct_1 was saved and received the ID: 4780\n",
- "The job lammps_struct_2 was saved and received the ID: 4781\n",
- "The job lammps_struct_3 was saved and received the ID: 4782\n",
- "The job lammps_struct_4 was saved and received the ID: 4783\n",
- "The job lammps_struct_5 was saved and received the ID: 4784\n",
- "The job lammps_struct_6 was saved and received the ID: 4785\n",
- "The job lammps_struct_7 was saved and received the ID: 4786\n",
- "The job lammps_struct_8 was saved and received the ID: 4787\n",
- "The job lammps_struct_9 was saved and received the ID: 4788\n",
- "The job lammps_struct_10 was saved and received the ID: 4789\n",
- "The job lammps_box was saved and received the ID: 4790\n"
+ "The job lammps_struct_0 was saved and received the ID: 248\n",
+ "The job lammps_struct_1 was saved and received the ID: 249\n",
+ "The job lammps_struct_2 was saved and received the ID: 250\n",
+ "The job lammps_struct_3 was saved and received the ID: 251\n",
+ "The job lammps_struct_4 was saved and received the ID: 252\n",
+ "The job lammps_struct_5 was saved and received the ID: 253\n",
+ "The job lammps_struct_6 was saved and received the ID: 254\n",
+ "The job lammps_struct_7 was saved and received the ID: 255\n",
+ "The job lammps_struct_8 was saved and received the ID: 256\n",
+ "The job lammps_struct_9 was saved and received the ID: 257\n",
+ "The job lammps_struct_10 was saved and received the ID: 258\n",
+ "The job lammps_box was saved and received the ID: 259\n",
+ "The job lammps_struct_0 was saved and received the ID: 260\n",
+ "The job lammps_struct_1 was saved and received the ID: 261\n",
+ "The job lammps_struct_2 was saved and received the ID: 262\n",
+ "The job lammps_struct_3 was saved and received the ID: 263\n",
+ "The job lammps_struct_4 was saved and received the ID: 264\n",
+ "The job lammps_struct_5 was saved and received the ID: 265\n",
+ "The job lammps_struct_6 was saved and received the ID: 266\n",
+ "The job lammps_struct_7 was saved and received the ID: 267\n",
+ "The job lammps_struct_8 was saved and received the ID: 268\n",
+ "The job lammps_struct_9 was saved and received the ID: 269\n",
+ "The job lammps_struct_10 was saved and received the ID: 270\n",
+ "The job lammps_box was saved and received the ID: 271\n",
+ "The job lammps_struct_0 was saved and received the ID: 272\n",
+ "The job lammps_struct_1 was saved and received the ID: 273\n",
+ "The job lammps_struct_2 was saved and received the ID: 274\n",
+ "The job lammps_struct_3 was saved and received the ID: 275\n",
+ "The job lammps_struct_4 was saved and received the ID: 276\n",
+ "The job lammps_struct_5 was saved and received the ID: 277\n",
+ "The job lammps_struct_6 was saved and received the ID: 278\n",
+ "The job lammps_struct_7 was saved and received the ID: 279\n",
+ "The job lammps_struct_8 was saved and received the ID: 280\n",
+ "The job lammps_struct_9 was saved and received the ID: 281\n",
+ "The job lammps_struct_10 was saved and received the ID: 282\n",
+ "The job lammps_box was saved and received the ID: 283\n",
+ "The job lammps_struct_0 was saved and received the ID: 284\n",
+ "The job lammps_struct_1 was saved and received the ID: 285\n",
+ "The job lammps_struct_2 was saved and received the ID: 286\n",
+ "The job lammps_struct_3 was saved and received the ID: 287\n",
+ "The job lammps_struct_4 was saved and received the ID: 288\n",
+ "The job lammps_struct_5 was saved and received the ID: 289\n",
+ "The job lammps_struct_6 was saved and received the ID: 290\n",
+ "The job lammps_struct_7 was saved and received the ID: 291\n",
+ "The job lammps_struct_8 was saved and received the ID: 292\n",
+ "The job lammps_struct_9 was saved and received the ID: 293\n",
+ "The job lammps_struct_10 was saved and received the ID: 294\n",
+ "The job lammps_box was saved and received the ID: 295\n",
+ "The job lammps_struct_0 was saved and received the ID: 296\n",
+ "The job lammps_struct_1 was saved and received the ID: 297\n",
+ "The job lammps_struct_2 was saved and received the ID: 298\n",
+ "The job lammps_struct_3 was saved and received the ID: 299\n",
+ "The job lammps_struct_4 was saved and received the ID: 300\n",
+ "The job lammps_struct_5 was saved and received the ID: 301\n",
+ "The job lammps_struct_6 was saved and received the ID: 302\n",
+ "The job lammps_struct_7 was saved and received the ID: 303\n",
+ "The job lammps_struct_8 was saved and received the ID: 304\n",
+ "The job lammps_struct_9 was saved and received the ID: 305\n",
+ "The job lammps_struct_10 was saved and received the ID: 306\n",
+ "The job lammps_box was saved and received the ID: 307\n"
]
}
],
@@ -2051,14 +2115,14 @@
{
"cell_type": "code",
"execution_count": 30,
- "id": "wrapped-conference",
+ "id": "danish-nerve",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
- "<Figure size 1296x864 with 6 Axes>"
+ "<Figure size 1296x864 with 10 Axes>"
]
},
"metadata": {
@@ -2097,7 +2161,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "international-worship",
+ "id": "median-syndication",
"metadata": {},
"outputs": [],
"source": []
@@ -2119,7 +2183,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.8"
+ "version": "3.8.6"
}
},
"nbformat": 4,