diff --git a/day_1/ex_01_introduction_to_pyiron.ipynb b/day_1/ex_01_introduction_to_pyiron.ipynb
index 2a00e422147f7f9cd050e2738099036bd13a8700..f138577404a4efff8aa0a3b9ab71d0921da85849 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": "advanced-saying",
"metadata": {},
"source": [
"# [**Workflows for atomistic simulations**](http://potentials.rub.de/) "
@@ -10,7 +10,7 @@
},
{
"cell_type": "markdown",
- "id": "numerous-egypt",
+ "id": "prerequisite-quarter",
"metadata": {},
"source": [
"## **Day 1 - Atomistic simulations with [pyiron](https://pyiron.org)**\n",
@@ -31,7 +31,7 @@
},
{
"cell_type": "markdown",
- "id": "beneficial-element",
+ "id": "black-radius",
"metadata": {},
"source": [
"### **Importing necessary libraries**\n",
@@ -42,7 +42,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "beneficial-republic",
+ "id": "departmental-alliance",
"metadata": {},
"outputs": [],
"source": [
@@ -53,7 +53,7 @@
},
{
"cell_type": "markdown",
- "id": "color-membership",
+ "id": "posted-gibraltar",
"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": "suited-county",
"metadata": {},
"outputs": [],
"source": [
@@ -71,7 +71,7 @@
},
{
"cell_type": "markdown",
- "id": "spectacular-shark",
+ "id": "victorian-southwest",
"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": "confident-confirmation",
"metadata": {},
"source": [
"### **Creation of a project instance**"
@@ -90,7 +90,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "knowing-rating",
+ "id": "resident-villa",
"metadata": {},
"outputs": [],
"source": [
@@ -99,7 +99,7 @@
},
{
"cell_type": "markdown",
- "id": "solid-protein",
+ "id": "nonprofit-stopping",
"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": "duplicate-hypothetical",
"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": "caring-arrow",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'groups': ['E_V_curve', 'E_V_curve_DFT'], 'nodes': ['lammps_job', 'sphinx_job']}"
+ "{'groups': [], 'nodes': []}"
]
},
"execution_count": 5,
@@ -149,7 +149,7 @@
},
{
"cell_type": "markdown",
- "id": "social-deadline",
+ "id": "processed-interstate",
"metadata": {},
"source": [
"### **Creating atomic structures**"
@@ -157,7 +157,7 @@
},
{
"cell_type": "markdown",
- "id": "cathedral-death",
+ "id": "environmental-tourist",
"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": "transparent-breathing",
"metadata": {},
"outputs": [
{
@@ -195,13 +195,13 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "verified-support",
+ "id": "front-access",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d9f4e7f5fa0a4d70bfcb7fcb805a3ddc",
+ "model_id": "84f435e61c344ce39f51358ec6ad903b",
"version_major": 2,
"version_minor": 0
},
@@ -213,7 +213,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3799d6daed5c4f438f09e8fd0f1ff66a",
+ "model_id": "e1d96e8c923b4683b75965b83441d3b0",
"version_major": 2,
"version_minor": 0
},
@@ -233,13 +233,13 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "chinese-output",
+ "id": "relative-memorial",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "13726dafec0e45a2854089529b295dde",
+ "model_id": "c84ad1b244b44d8d822d8a34e4527095",
"version_major": 2,
"version_minor": 0
},
@@ -260,7 +260,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "id": "mobile-tumor",
+ "id": "graduate-meter",
"metadata": {},
"outputs": [
{
@@ -273,7 +273,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "49a3693d4b0640bdb22dc17fc0e193d0",
+ "model_id": "ec656c43ad3b42a99ea4bd66f8aae095",
"version_major": 2,
"version_minor": 0
},
@@ -296,13 +296,13 @@
{
"cell_type": "code",
"execution_count": 10,
- "id": "immediate-share",
+ "id": "responsible-matrix",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "aa6674b5535b4ae8adc48584122cfc86",
+ "model_id": "e79238a4bc2b4dbe87ed5d5e16f97aca",
"version_major": 2,
"version_minor": 0
},
@@ -324,13 +324,13 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "dense-million",
+ "id": "boxed-newport",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "bfc0688cd5bc493082d2d7e794b5f18b",
+ "model_id": "f66b38a8c2304bffad66554c93c1ca29",
"version_major": 2,
"version_minor": 0
},
@@ -352,13 +352,13 @@
{
"cell_type": "code",
"execution_count": 12,
- "id": "mexican-difference",
+ "id": "blocked-conclusion",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b17bc1e85c7e42ca8952fe74e4822c9a",
+ "model_id": "20c695d1c0104e339b8457fc3b9269c4",
"version_major": 2,
"version_minor": 0
},
@@ -385,7 +385,7 @@
},
{
"cell_type": "markdown",
- "id": "twenty-spouse",
+ "id": "natural-granny",
"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": "naughty-audio",
"metadata": {},
"outputs": [],
"source": [
@@ -406,7 +406,7 @@
},
{
"cell_type": "markdown",
- "id": "imperial-prompt",
+ "id": "southwest-moses",
"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": "mysterious-hormone",
"metadata": {},
"outputs": [],
"source": [
@@ -425,7 +425,7 @@
},
{
"cell_type": "markdown",
- "id": "creative-railway",
+ "id": "medical-column",
"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": "mathematical-reliance",
"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": "assumed-terrain",
"metadata": {},
"outputs": [],
"source": [
@@ -475,7 +475,7 @@
},
{
"cell_type": "markdown",
- "id": "important-crowd",
+ "id": "strange-interpretation",
"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": "literary-proof",
"metadata": {},
"outputs": [],
"source": [
@@ -494,7 +494,7 @@
},
{
"cell_type": "markdown",
- "id": "pediatric-guidance",
+ "id": "ambient-municipality",
"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": "dedicated-sound",
"metadata": {},
"outputs": [
{
@@ -709,7 +709,7 @@
},
{
"cell_type": "markdown",
- "id": "equal-nylon",
+ "id": "sudden-seminar",
"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": "reflected-package",
"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: 168\n"
]
}
],
@@ -736,13 +736,13 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "headed-thinking",
+ "id": "underlying-lyric",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'groups': ['E_V_curve', 'E_V_curve_DFT'], 'nodes': ['lammps_job', 'sphinx_job']}"
+ "{'groups': [], 'nodes': ['lammps_job']}"
]
},
"execution_count": 20,
@@ -757,7 +757,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "republican-steps",
+ "id": "general-hardwood",
"metadata": {},
"outputs": [
{
@@ -801,386 +801,38 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>4354</td>\n",
+ " <td>168</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>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>/home/pyiron/</td>\n",
+ " <td>day_1/first_steps/</td>\n",
+ " <td>2021-03-09 09:30:08.840889</td>\n",
+ " <td>2021-03-09 09:30:15.206953</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>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>pyiron@jupyter-sudarsan#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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- " projectpath \\\n",
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+ " id status chemicalformula job subjob projectpath \\\n",
+ "0 168 finished Cu108 lammps_job /lammps_job /home/pyiron/ \n",
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- "15 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/ \n",
+ " project timestart timestop \\\n",
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+ " totalcputime computer hamilton hamversion parentid \\\n",
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- " computer hamilton hamversion parentid masterid \n",
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- "6 pyiron@cmdell17#1 Lammps 0.1 None None \n",
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- "12 pyiron@cmdell17#1 Sphinx 2.6.1 None None \n",
- "13 pyiron@cmdell17#1 Sphinx 2.6.1 None None \n",
- "14 pyiron@cmdell17#1 Sphinx 2.6.1 None None \n",
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+ " masterid \n",
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},
"execution_count": 21,
@@ -1194,29 +846,19 @@
},
{
"cell_type": "markdown",
- "id": "french-vertical",
+ "id": "unable-illustration",
"metadata": {},
"source": [
- "## Analysing a calculation"
+ "## **Analysing a calculation**"
]
},
{
"cell_type": "code",
"execution_count": 22,
- "id": "after-experience",
+ "id": "perceived-anthony",
"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"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "%%time\n",
"# Load the job\n",
"job_loaded = pr['lammps_job']"
]
@@ -1224,7 +866,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "id": "featured-locking",
+ "id": "adequate-warehouse",
"metadata": {},
"outputs": [
{
@@ -1239,13 +881,13 @@
}
],
"source": [
- "job_loaded\n"
+ "job_loaded"
]
},
{
"cell_type": "code",
"execution_count": 24,
- "id": "interior-induction",
+ "id": "focal-accident",
"metadata": {},
"outputs": [
{
@@ -1266,13 +908,13 @@
{
"cell_type": "code",
"execution_count": 25,
- "id": "manual-practice",
+ "id": "conditional-kuwait",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ee05400e20d94507ac3912835b6b33b5",
+ "model_id": "fc4b74f3bb0241c39c519c1ad7a47a63",
"version_major": 2,
"version_minor": 0
},
@@ -1292,13 +934,13 @@
{
"cell_type": "code",
"execution_count": 26,
- "id": "printable-soldier",
+ "id": "sealed-system",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7f833d0e308842c2924c519c610d085d",
+ "model_id": "4fdf13d51a3c4786b7a35c27dd5bbbd4",
"version_major": 2,
"version_minor": 0
},
@@ -1317,7 +959,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "id": "adjusted-movie",
+ "id": "facial-lottery",
"metadata": {},
"outputs": [
{
@@ -1344,7 +986,7 @@
{
"cell_type": "code",
"execution_count": 28,
- "id": "bearing-receipt",
+ "id": "thick-discipline",
"metadata": {},
"outputs": [
{
@@ -1373,7 +1015,7 @@
},
{
"cell_type": "markdown",
- "id": "parallel-bunch",
+ "id": "active-mixer",
"metadata": {},
"source": [
"### **Running an atomistic calculation using DFT (with SPHInX)**"
@@ -1382,16 +1024,14 @@
{
"cell_type": "code",
"execution_count": 29,
- "id": "stone-feedback",
+ "id": "breeding-mexican",
"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: 175\n"
]
}
],
@@ -1407,7 +1047,7 @@
{
"cell_type": "code",
"execution_count": 30,
- "id": "tropical-complement",
+ "id": "coordinate-translation",
"metadata": {},
"outputs": [
{
@@ -1428,13 +1068,13 @@
{
"cell_type": "code",
"execution_count": 31,
- "id": "superior-cisco",
+ "id": "statutory-introduction",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([-5386.42735597])"
+ "array([-5386.42735492])"
]
},
"execution_count": 31,
@@ -1449,7 +1089,7 @@
{
"cell_type": "code",
"execution_count": 32,
- "id": "joined-prairie",
+ "id": "chief-gallery",
"metadata": {},
"outputs": [
{
@@ -1470,12 +1110,12 @@
{
"cell_type": "code",
"execution_count": 33,
- "id": "flexible-horizontal",
+ "id": "protecting-weight",
"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 +1139,7 @@
},
{
"cell_type": "markdown",
- "id": "french-spare",
+ "id": "current-challenge",
"metadata": {},
"source": [
"## **Task 1: Energy volume curve for Al**"
@@ -1508,7 +1148,7 @@
{
"cell_type": "code",
"execution_count": 34,
- "id": "resistant-vegetation",
+ "id": "collectible-router",
"metadata": {},
"outputs": [],
"source": [
@@ -1519,7 +1159,7 @@
{
"cell_type": "code",
"execution_count": 35,
- "id": "bibliographic-chancellor",
+ "id": "returning-chest",
"metadata": {},
"outputs": [],
"source": [
@@ -1530,13 +1170,13 @@
{
"cell_type": "code",
"execution_count": 36,
- "id": "regulated-marriage",
+ "id": "hazardous-leonard",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(-345.776978906969, 1337.89883611173)"
+ "(-345.776978906089, 1337.89883612206)"
]
},
"execution_count": 36,
@@ -1551,33 +1191,24 @@
{
"cell_type": "code",
"execution_count": 37,
- "id": "blond-abortion",
+ "id": "another-cache",
"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: 176\n",
+ "The job job_a_3_5 was saved and received the ID: 177\n",
+ "The job job_a_3_6 was saved and received the ID: 178\n",
+ "The job job_a_3_7 was saved and received the ID: 180\n",
+ "The job job_a_3_8 was saved and received the ID: 183\n",
+ "The job job_a_3_9 was saved and received the ID: 185\n",
+ "The job job_a_4_0 was saved and received the ID: 188\n"
]
}
],
"source": [
- "%%time\n",
"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",
@@ -1593,13 +1224,13 @@
{
"cell_type": "code",
"execution_count": 38,
- "id": "imported-birmingham",
+ "id": "vietnamese-cooper",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(['E_V_curve', 'E_V_curve_DFT'], ['lammps_job', 'sphinx_job'])"
+ "(['E_V_curve'], ['lammps_job', 'sphinx_job'])"
]
},
"execution_count": 38,
@@ -1614,7 +1245,7 @@
{
"cell_type": "code",
"execution_count": 39,
- "id": "demographic-publicity",
+ "id": "destroyed-incident",
"metadata": {},
"outputs": [
{
@@ -1658,17 +1289,17 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>4356</td>\n",
+ " <td>176</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 09:30:22.540993</td>\n",
+ " <td>2021-03-09 09:30:22.956864</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-sudarsan#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1676,17 +1307,17 @@
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>4357</td>\n",
+ " <td>177</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 09:30:23.763351</td>\n",
+ " <td>2021-03-09 09:30:24.226511</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-sudarsan#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1694,17 +1325,17 @@
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>4358</td>\n",
+ " <td>178</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 09:30:25.307550</td>\n",
+ " <td>2021-03-09 09:30:25.811752</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-sudarsan#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1712,17 +1343,17 @@
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>4359</td>\n",
+ " <td>180</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 09:30:27.023289</td>\n",
+ " <td>2021-03-09 09:30:27.631846</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-sudarsan#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1730,17 +1361,17 @@
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>4360</td>\n",
+ " <td>183</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 09:30:29.352758</td>\n",
+ " <td>2021-03-09 09:30:29.904205</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-sudarsan#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1748,17 +1379,17 @@
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
- " <td>4361</td>\n",
+ " <td>185</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 09:30:31.536258</td>\n",
+ " <td>2021-03-09 09:30:32.210804</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-sudarsan#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1766,17 +1397,17 @@
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
- " <td>4362</td>\n",
+ " <td>188</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 09:30:34.056451</td>\n",
+ " <td>2021-03-09 09:30:34.612759</td>\n",
" <td>0.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-sudarsan#1</td>\n",
" <td>Lammps</td>\n",
" <td>0.1</td>\n",
" <td>None</td>\n",
@@ -1787,41 +1418,41 @@
"</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",
+ "0 176 finished Cu job_a_3_4 /job_a_3_4 /home/pyiron/ \n",
+ "1 177 finished Cu job_a_3_5 /job_a_3_5 /home/pyiron/ \n",
+ "2 178 finished Cu job_a_3_6 /job_a_3_6 /home/pyiron/ \n",
+ "3 180 finished Cu job_a_3_7 /job_a_3_7 /home/pyiron/ \n",
+ "4 183 finished Cu job_a_3_8 /job_a_3_8 /home/pyiron/ \n",
+ "5 185 finished Cu job_a_3_9 /job_a_3_9 /home/pyiron/ \n",
+ "6 188 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 09:30:22.540993 \n",
+ "1 day_1/first_steps/E_V_curve/ 2021-03-09 09:30:23.763351 \n",
+ "2 day_1/first_steps/E_V_curve/ 2021-03-09 09:30:25.307550 \n",
+ "3 day_1/first_steps/E_V_curve/ 2021-03-09 09:30:27.023289 \n",
+ "4 day_1/first_steps/E_V_curve/ 2021-03-09 09:30:29.352758 \n",
+ "5 day_1/first_steps/E_V_curve/ 2021-03-09 09:30:31.536258 \n",
+ "6 day_1/first_steps/E_V_curve/ 2021-03-09 09:30:34.056451 \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 \\\n",
+ "0 2021-03-09 09:30:22.956864 0.0 pyiron@jupyter-sudarsan#1 \n",
+ "1 2021-03-09 09:30:24.226511 0.0 pyiron@jupyter-sudarsan#1 \n",
+ "2 2021-03-09 09:30:25.811752 0.0 pyiron@jupyter-sudarsan#1 \n",
+ "3 2021-03-09 09:30:27.631846 0.0 pyiron@jupyter-sudarsan#1 \n",
+ "4 2021-03-09 09:30:29.904205 0.0 pyiron@jupyter-sudarsan#1 \n",
+ "5 2021-03-09 09:30:32.210804 0.0 pyiron@jupyter-sudarsan#1 \n",
+ "6 2021-03-09 09:30:34.612759 0.0 pyiron@jupyter-sudarsan#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 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 "
+ " hamilton hamversion parentid masterid \n",
+ "0 Lammps 0.1 None None \n",
+ "1 Lammps 0.1 None None \n",
+ "2 Lammps 0.1 None None \n",
+ "3 Lammps 0.1 None None \n",
+ "4 Lammps 0.1 None None \n",
+ "5 Lammps 0.1 None None \n",
+ "6 Lammps 0.1 None None "
]
},
"execution_count": 39,
@@ -1836,27 +1467,9 @@
{
"cell_type": "code",
"execution_count": 40,
- "id": "composed-architecture",
+ "id": "based-wallet",
"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"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Energy [eV]')"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- },
{
"data": {
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\n",
@@ -1871,7 +1484,6 @@
}
],
"source": [
- "%%time\n",
"# Analysing the data\n",
"vol_list = list()\n",
"energy_list = list()\n",
@@ -1891,27 +1503,9 @@
{
"cell_type": "code",
"execution_count": 41,
- "id": "challenging-knife",
+ "id": "indirect-uruguay",
"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"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Energy [eV]')"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- },
{
"data": {
"image/png": 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\n",
@@ -1926,7 +1520,6 @@
}
],
"source": [
- "%%time\n",
"# Analysing the data\n",
"vol_list = list()\n",
"energy_list = list()\n",
@@ -1945,7 +1538,7 @@
},
{
"cell_type": "markdown",
- "id": "chemical-survival",
+ "id": "impressed-spank",
"metadata": {},
"source": [
"## **Task 2: E-V curves for DFT**"
@@ -1954,35 +1547,116 @@
{
"cell_type": "code",
"execution_count": 42,
- "id": "retained-monkey",
+ "id": "maritime-divide",
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job job_a_3_4 was saved and received the ID: 193\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: 198\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: 199\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_3_7 was saved and received the ID: 201\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: 207\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: 213\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_4_0 was saved and received the ID: 217\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"
]
}
],
"source": [
- "%%time\n",
"pr_ev = pr.create_group(\"E_V_curve_DFT\") # 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",
@@ -2001,30 +1675,12 @@
{
"cell_type": "code",
"execution_count": 43,
- "id": "descending-insurance",
+ "id": "moving-manufacturer",
"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"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x7f73835ac4c0>"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- },
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2036,7 +1692,6 @@
}
],
"source": [
- "%%time\n",
"# Analysing the data\n",
"group_list = pr.list_groups()\n",
"\n",
@@ -2057,24 +1712,26 @@
},
{
"cell_type": "markdown",
- "id": "coordinated-partner",
+ "id": "round-possibility",
"metadata": {},
"source": [
- "## **Advanced pyiron: Automated workflows and analysis tools**"
+ "## **Advanced pyiron: Automated workflows and analysis tools**\n",
+ "\n",
+ "While we could in principle obtain thee E-V cureves by setting up and analyzing the calculations manually as done above, we could also use predefined workflows in pyiron which does this automatically"
]
},
{
"cell_type": "code",
"execution_count": 44,
- "id": "cooperative-documentary",
+ "id": "floppy-sustainability",
"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']"
+ "['2004--Lee-B-J--Cu-Ni--LAMMPS--ipr1',\n",
+ " '2004--Zhou-X-W--Cu--LAMMPS--ipr2',\n",
+ " '2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2']"
]
},
"execution_count": 44,
@@ -2085,14 +1742,14 @@
"source": [
"num_pot = 3\n",
"pot_finder = pr.inspect_emperical_potentials()\n",
- "pot_list = pot_finder.find(\"Cu\").Name.to_list()[:num_pot]\n",
+ "pot_list = pot_finder.find(\"Cu\").Name.to_list()[20:20+num_pot]\n",
"pot_list"
]
},
{
"cell_type": "code",
"execution_count": 45,
- "id": "detailed-empire",
+ "id": "still-nelson",
"metadata": {},
"outputs": [],
"source": [
@@ -2102,32 +1759,76 @@
},
{
"cell_type": "code",
- "execution_count": 46,
- "id": "meaningful-europe",
+ "execution_count": 53,
+ "id": "heard-drain",
"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_2004__Lee_B_J__Cu_Ni__LAMMPS__i was saved and received the ID: 236\n",
+ "The job strain_0_9 was saved and received the ID: 237\n",
+ "The job strain_0_925 was saved and received the ID: 238\n",
+ "The job strain_0_95 was saved and received the ID: 239\n",
+ "The job strain_0_975 was saved and received the ID: 240\n",
+ "The job strain_1_0 was saved and received the ID: 241\n",
+ "The job strain_1_025 was saved and received the ID: 243\n",
+ "The job strain_1_05 was saved and received the ID: 244\n",
+ "The job strain_1_075 was saved and received the ID: 245\n",
+ "The job strain_1_1 was saved and received the ID: 246\n",
+ "job_id: 237 finished\n",
+ "job_id: 238 finished\n",
+ "job_id: 239 finished\n",
+ "job_id: 240 finished\n",
+ "job_id: 241 finished\n",
+ "job_id: 243 finished\n",
+ "job_id: 244 finished\n",
+ "job_id: 245 finished\n",
+ "job_id: 246 finished\n",
+ "The job murn_ref_2004__Zhou_X_W__Cu__LAMMPS__ipr was saved and received the ID: 247\n",
+ "The job strain_0_9 was saved and received the ID: 248\n",
+ "The job strain_0_925 was saved and received the ID: 249\n",
+ "The job strain_0_95 was saved and received the ID: 250\n",
+ "The job strain_0_975 was saved and received the ID: 252\n",
+ "The job strain_1_0 was saved and received the ID: 253\n",
+ "The job strain_1_025 was saved and received the ID: 254\n",
+ "The job strain_1_05 was saved and received the ID: 255\n",
+ "The job strain_1_075 was saved and received the ID: 256\n",
+ "The job strain_1_1 was saved and received the ID: 257\n",
+ "job_id: 248 finished\n",
+ "job_id: 249 finished\n",
+ "job_id: 250 finished\n",
+ "job_id: 252 finished\n",
+ "job_id: 253 finished\n",
+ "job_id: 254 finished\n",
+ "job_id: 255 finished\n",
+ "job_id: 256 finished\n",
+ "job_id: 257 finished\n",
+ "The job murn_ref_2004__Zhou_X_W__Cu_Ag_Au__LAMMP was saved and received the ID: 258\n",
+ "The job strain_0_9 was saved and received the ID: 259\n",
+ "The job strain_0_925 was saved and received the ID: 260\n",
+ "The job strain_0_95 was saved and received the ID: 262\n",
+ "The job strain_0_975 was saved and received the ID: 263\n",
+ "The job strain_1_0 was saved and received the ID: 264\n",
+ "The job strain_1_025 was saved and received the ID: 265\n",
+ "The job strain_1_05 was saved and received the ID: 266\n",
+ "The job strain_1_075 was saved and received the ID: 267\n",
+ "The job strain_1_1 was saved and received the ID: 268\n",
+ "job_id: 259 finished\n",
+ "job_id: 260 finished\n",
+ "job_id: 262 finished\n",
+ "job_id: 263 finished\n",
+ "job_id: 264 finished\n",
+ "job_id: 265 finished\n",
+ "job_id: 266 finished\n",
+ "job_id: 267 finished\n",
+ "job_id: 268 finished\n"
]
}
],
"source": [
- "%%time\n",
- "# Automated Murnaghan\n",
- "# pyiron tables\n",
+ "# Automated Murnaghan using pyiron tables\n",
"\n",
"pr_murn = Project(\"murn_auto\") \n",
"\n",
@@ -2141,6 +1842,7 @@
" # Creating a Murnaghan workflow (char names not to exceed 50 chars)\n",
" job_name = \"murn_ref_{}\".format(clean_project_name(pot))[:40]\n",
" \n",
+ " # The job type 'Murnaghan' sets up the appropriate workflow \n",
" murn_job = lammps_job.create_job(pr.job_type.Murnaghan, job_name)\n",
" murn_job.input[\"num_points\"] = 9\n",
" murn_job.run()"
@@ -2148,13 +1850,13 @@
},
{
"cell_type": "code",
- "execution_count": 47,
- "id": "signal-agreement",
+ "execution_count": 54,
+ "id": "accomplished-aggregate",
"metadata": {},
"outputs": [
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2171,17 +1873,17 @@
},
{
"cell_type": "code",
- "execution_count": 48,
- "id": "identical-stuart",
+ "execution_count": 55,
+ "id": "deluxe-going",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(11.808803921590759, 141.83475481259447)"
+ "(11.809747918737973, 135.83377204846934)"
]
},
- "execution_count": 48,
+ "execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
@@ -2192,17 +1894,17 @@
},
{
"cell_type": "code",
- "execution_count": 49,
- "id": "immune-resident",
+ "execution_count": 56,
+ "id": "fleet-rogers",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "3.614836295562474"
+ "3.6149326164726543"
]
},
- "execution_count": 49,
+ "execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
@@ -2211,16 +1913,26 @@
"np.linalg.norm(murn_job[\"output/structure/cell/cell\"][0]) * np.sqrt(2)"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "monthly-doubt",
+ "metadata": {},
+ "source": [
+ "We now analyze the data using our in-built pyiron tables class"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 50,
- "id": "sunrise-reach",
+ "execution_count": 57,
+ "id": "uniform-armstrong",
"metadata": {},
"outputs": [],
"source": [
+ "# A filter function that selects only Murnaghan jobs\n",
"def get_only_murn(job_table):\n",
" return (job_table.hamilton == \"Murnaghan\") & (job_table.status == \"finished\")\n",
"\n",
+ "# Functions to obtain output from Murnaghan jobs\n",
"def get_eq_vol(job_path):\n",
" return job_path[\"output/equilibrium_volume\"]\n",
"\n",
@@ -2228,43 +1940,38 @@
" return np.linalg.norm(job_path[\"output/structure/cell/cell\"][0]) * np.sqrt(2)\n",
"\n",
"def get_eq_bm(job_path):\n",
- " return job_path[\"output/equilibrium_bulk_modulus\"]"
+ " return job_path[\"output/equilibrium_bulk_modulus\"]\n",
+ "\n",
+ "def get_potential(job_path):\n",
+ " return job_path[\"lammps_ref/input/potential/Name\"]"
]
},
{
"cell_type": "code",
- "execution_count": 51,
- "id": "certified-acting",
+ "execution_count": 58,
+ "id": "foster-yellow",
"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, 753.56it/s]\n",
+ "100%|██████████| 3/3 [00:00<00:00, 77.20it/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: 269\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"
]
},
{
@@ -2289,6 +1996,7 @@
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
+ " <th>potential</th>\n",
" <th>a</th>\n",
" <th>eq_vol</th>\n",
" <th>eq_bm</th>\n",
@@ -2297,61 +2005,54 @@
" <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>236</td>\n",
+ " <td>2004--Lee-B-J--Cu-Ni--LAMMPS--ipr1</td>\n",
+ " <td>3.521448</td>\n",
+ " <td>10.917012</td>\n",
+ " <td>179.040468</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>247</td>\n",
+ " <td>2004--Zhou-X-W--Cu--LAMMPS--ipr2</td>\n",
+ " <td>3.614921</td>\n",
+ " <td>11.809634</td>\n",
+ " <td>135.833414</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>258</td>\n",
+ " <td>2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2</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",
- " </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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"
+ " job_id potential a eq_vol \\\n",
+ "0 236 2004--Lee-B-J--Cu-Ni--LAMMPS--ipr1 3.521448 10.917012 \n",
+ "1 247 2004--Zhou-X-W--Cu--LAMMPS--ipr2 3.614921 11.809634 \n",
+ "2 258 2004--Zhou-X-W--Cu-Ag-Au--LAMMPS--ipr2 3.614933 11.809748 \n",
+ "\n",
+ " eq_bm \n",
+ "0 179.040468 \n",
+ "1 135.833414 \n",
+ "2 135.833772 "
]
},
- "execution_count": 51,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "%%time\n",
+ "# Creating a pyiron table and processing output\n",
"table = pr_murn.create.table(\"table_murn\", delete_existing_job=True)\n",
"table.db_filter_function = get_only_murn\n",
+ "table.add[\"potential\"] = get_potential\n",
"table.add[\"a\"] = get_eq_lp\n",
"table.add[\"eq_vol\"] = get_eq_vol\n",
"table.add[\"eq_bm\"] = get_eq_bm\n",
@@ -2363,7 +2064,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "reasonable-liability",
+ "id": "contrary-wesley",
"metadata": {},
"outputs": [],
"source": []
@@ -2385,7 +2086,7 @@
"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..d746423038e36d6fabbf0ace2e7cd4dadd94ab1b 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": "previous-lotus",
"metadata": {},
"source": [
"# [**Workflows for atomistic simulations**](http://potentials.rub.de/) "
@@ -10,7 +10,7 @@
},
{
"cell_type": "markdown",
- "id": "straight-bicycle",
+ "id": "solid-explosion",
"metadata": {},
"source": [
"## **Day 1 - Atomistic simulations with [pyiron](https://pyiron.org)**\n",
@@ -29,7 +29,7 @@
},
{
"cell_type": "markdown",
- "id": "durable-leone",
+ "id": "apparent-assembly",
"metadata": {},
"source": [
"## **Importing necessary modules and creating a project**\n",
@@ -40,7 +40,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "novel-wisconsin",
+ "id": "fitting-testing",
"metadata": {},
"outputs": [],
"source": [
@@ -52,7 +52,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "sitting-religious",
+ "id": "mature-bearing",
"metadata": {},
"outputs": [],
"source": [
@@ -62,7 +62,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "technical-newport",
+ "id": "considered-karma",
"metadata": {},
"outputs": [],
"source": [
@@ -71,7 +71,7 @@
},
{
"cell_type": "markdown",
- "id": "based-kentucky",
+ "id": "controlled-david",
"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": "wrong-pickup",
"metadata": {},
"outputs": [],
"source": [
@@ -94,8 +94,8 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "id": "filled-natural",
+ "execution_count": 5,
+ "id": "current-vanilla",
"metadata": {},
"outputs": [],
"source": [
@@ -105,7 +105,7 @@
},
{
"cell_type": "markdown",
- "id": "married-storm",
+ "id": "dominican-northwest",
"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": "concrete-background",
"metadata": {},
"outputs": [],
"source": [
@@ -127,7 +127,7 @@
},
{
"cell_type": "markdown",
- "id": "acquired-missile",
+ "id": "worse-scheduling",
"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": "changed-shame",
"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",
+ " 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",
- " 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",
+ " 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",
- " 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",
- "\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",
- "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",
- "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",
- "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",
- "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",
- "\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": "ultimate-duncan",
"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": "disabled-computer",
"metadata": {},
"outputs": [],
"source": [
@@ -827,8 +289,8 @@
},
{
"cell_type": "code",
- "execution_count": 21,
- "id": "outside-bhutan",
+ "execution_count": 9,
+ "id": "aggregate-wilderness",
"metadata": {},
"outputs": [],
"source": [
@@ -843,7 +305,7 @@
},
{
"cell_type": "markdown",
- "id": "minus-blink",
+ "id": "welsh-commercial",
"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": "continuing-upset",
"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: 303\n"
]
}
],
@@ -877,15 +339,15 @@
},
{
"cell_type": "code",
- "execution_count": 28,
- "id": "billion-shade",
+ "execution_count": 11,
+ "id": "listed-occurrence",
"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: 304\n"
]
}
],
@@ -900,17 +362,17 @@
},
{
"cell_type": "code",
- "execution_count": 29,
- "id": "constitutional-throw",
+ "execution_count": 12,
+ "id": "sound-bathroom",
"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": "processed-liability",
"metadata": {},
"source": [
"We now add these structures to the dataset"
@@ -929,31 +391,42 @@
},
{
"cell_type": "code",
- "execution_count": 33,
- "id": "italian-cleanup",
+ "execution_count": 19,
+ "id": "trained-target",
"metadata": {},
"outputs": [],
"source": [
- "for job_name in [\"lammps_job_vac\", \"lammps_job_surf\"]:\n",
- " job_md = pr.load(job_name)\n",
+ "for job_md in pr.iter_jobs(status=\"finished\"):\n",
" pos = job_md[\"output/generic/positions\"]\n",
- " traj_length = len(pos)\n",
- " stride = 10\n",
- " for i in range(0, traj_length, stride):\n",
- " container.include_job(job_md, iteration_step=i)"
+ " if pos is not None:\n",
+ " traj_length = len(pos)\n",
+ " stride = 10\n",
+ " for i in range(0, traj_length, stride):\n",
+ " container.include_job(job_md, iteration_step=i)"
]
},
{
"cell_type": "code",
- "execution_count": 34,
- "id": "exotic-asset",
+ "execution_count": 14,
+ "id": "attended-drama",
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The job dataset_example was saved and received the ID: 306\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": "signal-establishment",
"metadata": {},
"outputs": [
{
@@ -1009,17 +482,17 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>4332</td>\n",
+ " <td>303</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 09:38:28.337496</td>\n",
+ " <td>2021-03-09 09:38:33.222851</td>\n",
+ " <td>4.0</td>\n",
+ " <td>pyiron@jupyter-sudarsan#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>304</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>4.0</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>/home/pyiron/</td>\n",
+ " <td>day_1/creating_datasets/</td>\n",
+ " <td>2021-03-09 09:38:34.181033</td>\n",
+ " <td>2021-03-09 09:38:39.609826</td>\n",
+ " <td>5.0</td>\n",
+ " <td>pyiron@jupyter-sudarsan#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>306</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 09:38:48.660175</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
- " <td>pyiron@cmdell17#1</td>\n",
+ " <td>pyiron@jupyter-sudarsan#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",
+ "0 303 finished Cu107 lammps_job_vac /lammps_job_vac \n",
+ "1 304 finished Cu128 lammps_job_surf /lammps_job_surf \n",
+ "2 306 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 09:38:28.337496 \n",
+ "1 /home/pyiron/ day_1/creating_datasets/ 2021-03-09 09:38:34.181033 \n",
+ "2 /home/pyiron/ day_1/creating_datasets/ 2021-03-09 09:38:48.660175 \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 09:38:33.222851 4.0 pyiron@jupyter-sudarsan#1 \n",
+ "1 2021-03-09 09:38:39.609826 5.0 pyiron@jupyter-sudarsan#1 \n",
+ "2 NaT NaN pyiron@jupyter-sudarsan#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": "lyric-blair",
"metadata": {},
"source": [
"## **Reloading the dataset**\n",
@@ -1108,8 +581,8 @@
},
{
"cell_type": "code",
- "execution_count": 35,
- "id": "numeric-museum",
+ "execution_count": 16,
+ "id": "swiss-catering",
"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",
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- "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",
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- "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",
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+ "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": "premier-least",
"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": "difficult-cartoon",
"metadata": {},
"outputs": [],
"source": [
@@ -1707,8 +1180,8 @@
},
{
"cell_type": "code",
- "execution_count": 37,
- "id": "becoming-integral",
+ "execution_count": 18,
+ "id": "analyzed-bargain",
"metadata": {},
"outputs": [],
"source": [
@@ -1717,7 +1190,7 @@
},
{
"cell_type": "markdown",
- "id": "novel-usage",
+ "id": "serious-carry",
"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": "simple-packet",
"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,