Commit 5061e307 authored by Jan Janssen's avatar Jan Janssen
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Executed all the notebooks again on workshop.pyiron.org

parent 4307dfbe
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# [**Workflows for atomistic simulations**](http://potentials.rub.de/)
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## **Day 1 - Atomistic simulations with [pyiron](https://pyiron.org)**
### **Exercise 1: Introduction to atomistic simulations with pyiron**
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The aim of this exercise is to make you familiar with:
* A general overview of what pyiron can do
* How to set up atomic structures and run atomistic simulation codes through pyiron
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### **Importing necessary libraries**
As a first step we import the libraries [numpy](http://www.numpy.org/) for data analysis and [matplotlib](https://matplotlib.org/) for visualization.
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``` python
import numpy as np
%matplotlib inline
import matplotlib.pylab as plt
```
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Fundamentally, we only need to import one module from `pyiron`: the `Project` class
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``` python
from pyiron import Project
```
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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.
We now create a pyiron Project named 'first_steps'.
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### **Creation of a project instance**
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``` python
pr = Project("first_steps")
```
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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.
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``` python
pr.path
```
%%%% Output: execute_result
'/home/surendralal/notebooks/pyiron_potentialfit/day_1/first_steps/'
'/home/pyiron/day_1/first_steps/'
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``` python
pr
```
%%%% Output: execute_result
{'groups': ['E_V_curve', 'E_V_curve_DFT'], 'nodes': ['lammps_job', 'sphinx_job']}
{'groups': ['E_V_curve', 'E_V_curve_DFT'], 'nodes': []}
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### **Creating atomic structures**
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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.
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``` python
# Creating a bulk fcc cubic unitcell
Cu_unitcell_cubic = pr.create_ase_bulk('Cu', cubic=True, a=3.61)
Cu_unitcell_cubic
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Cu: [1.805 1.805 0. ]
pbc: [ True True True]
cell:
Cell([3.61, 3.61, 3.61])
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``` python
Cu_supercell_3_3_3 = Cu_unitcell_cubic.repeat([3, 3, 3])
Cu_supercell_3_3_3.plot3d(particle_size=2)
```
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%%%% Output: display_data
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``` python
# Creating a bulk fcc primitive unitcell and cupercell
Cu_unitcell_primitive = pr.create_ase_bulk('Cu', a=4.01)
Cu_unitcell_primitive.repeat([3, 3, 3]).plot3d(particle_size=2)
```
%%%% Output: display_data
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``` python
# Creating a vacancy in a supercell
Cu_vacancy = Cu_supercell_3_3_3.copy()
del Cu_vacancy[0] # Deleting the first atom
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```
%%%% Output: display_data
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``` python
# Creating a fcc111 surface supercell
num_layers = 4
Cu_fcc_111 = pr.create_surface("Cu", surface_type="fcc111", size=(4, 4, num_layers), vacuum=12, orthogonal=True)
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```
%%%% Output: display_data
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``` python
# Atom in a box
cell = np.eye(3) * 10
Cu_atom_box = pr.create_atoms("Cu", cell=cell, scaled_positions=[[0.5, 0.5, 0.5]])
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```
%%%% Output: display_data
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``` python
# Cu-Cu dimer
cell = np.eye(3) * 10
Cu_atom_1 = pr.create_atoms("Cu", cell=cell, scaled_positions=[[0.5, 0.5, 0.5]])
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```
%%%% Output: display_data
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### **Running an atomistic calculation using interatomic potentials (with LAMMPS)**
Once we have an atomic structure, we can set up a simulation "job" of any atomistic simulation that is intergrated within pyiron. In this section, we are going to use the popular [LAMMPS code](https://lammps.sandia.gov/).
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``` python
# Create a job
job_lammps = pr.create.job.Lammps(job_name="lammps_job")
```
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Every atomistic simulation code needs an input atomic structure. We use the Cu supercell structure we created earlier
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``` python
# Assign an atomic structure to the job
job_lammps.structure = Cu_supercell_3_3_3
```
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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
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``` python
# See available potentials
job_lammps.list_potentials()[50:60]
```
%%%% Output: execute_result
['EAM_Dynamo_LiuLiuBorucki_1999_AlCu__MO_020851069572_000',
'EAM_Dynamo_MendelevKing_2008_Cu__MO_748636486270_005',
'EAM_Dynamo_MendelevKramerBecker_2008_Cu__MO_945691923444_005',
'EAM_Dynamo_MendelevKramerOtt_2009_CuZr__MO_600021860456_005',
'EAM_Dynamo_MendelevSordeletKramer_2007_CuZr__MO_120596890176_005',
'EAM_Dynamo_MishinMehlPapaconstantopoulos_2001_Cu__MO_346334655118_005',
'EAM_Dynamo_OnatDurukanoglu_2014_CuNi__MO_592013496703_005',
'EAM_Dynamo_WilliamsMishinHamilton_2006_CuAg__MO_128703483589_005',
'EAM_Dynamo_WuTrinkle_2009_CuAg__MO_270337113239_005',
'EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_CuAgAu__MO_318213562153_000']
['2016--Borovikov-V--fictional-Cu-31--LAMMPS--ipr1',
'2016--Borovikov-V--fictional-Cu-32--LAMMPS--ipr1',
'2016--Borovikov-V--fictional-Cu-33--LAMMPS--ipr1',
'2016--Borovikov-V--fictional-Cu-34--LAMMPS--ipr1',
'2016--Zhou-X-W--Al-Cu--LAMMPS--ipr2',
'2017--Kim-J-S--Cu-Pt--LAMMPS--ipr1',
'2018--Etesami-S-A--Cu--LAMMPS--ipr1',
'2018--Farkas-D--Fe-Ni-Cr-Co-Cu--LAMMPS--ipr2',
'2018--Jeong-G-U--Pd-Cu--LAMMPS--ipr1',
'2018--Zhou-X-W--Al-Cu-H--LAMMPS--ipr1']
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``` python
# Choose one of these potentials
job_lammps.potential = '2012--Mendelev-M-I--Cu--LAMMPS--ipr1'
```
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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
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``` python
# specify calculation details: in this case: MD at 800 K in the NPT ensemble (pressure=0) for 10000 steps
job_lammps.calc_md(temperature=800, pressure=0, n_ionic_steps=10000)
```
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We can now see how pyiron sets-up the corresponding LAMMPS input
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``` python
job_lammps.input.control
```
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13
14
15
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Once the `run()` commmand is called, pyiron creates necessary input files, calls the simulation code, and finally parses and stores the output.
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``` python
job_lammps.run()
```
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``` python
pr
```
%%%% Output: execute_result
{'groups': ['E_V_curve', 'E_V_curve_DFT'], 'nodes': ['lammps_job', 'sphinx_job']}
{'groups': ['E_V_curve', 'E_V_curve_DFT'], 'nodes': ['lammps_job']}
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``` python
pr.job_table()
```
%%%% Output: execute_result
id status chemicalformula job subjob \
0 4354 finished Cu108 lammps_job /lammps_job
1 4355 finished Cu sphinx_job /sphinx_job
2 4356 finished Cu job_a_3_4 /job_a_3_4
3 4357 finished Cu job_a_3_5 /job_a_3_5
4 4358 finished Cu job_a_3_6 /job_a_3_6
5 4359 finished Cu job_a_3_7 /job_a_3_7
6 4360 finished Cu job_a_3_8 /job_a_3_8
7 4361 finished Cu job_a_3_9 /job_a_3_9
8 4362 finished Cu job_a_4_0 /job_a_4_0
9 4363 finished Cu job_a_3_4 /job_a_3_4
10 4364 finished Cu job_a_3_5 /job_a_3_5
11 4365 finished Cu job_a_3_6 /job_a_3_6
12 4366 finished Cu job_a_3_7 /job_a_3_7
13 4367 finished Cu job_a_3_8 /job_a_3_8
14 4368 finished Cu job_a_3_9 /job_a_3_9
15 4369 finished Cu job_a_4_0 /job_a_4_0
projectpath \
0 /home/surendralal/
1 /home/surendralal/
2 /home/surendralal/
3 /home/surendralal/
4 /home/surendralal/
5 /home/surendralal/
6 /home/surendralal/
7 /home/surendralal/
8 /home/surendralal/
9 /home/surendralal/
10 /home/surendralal/
11 /home/surendralal/
12 /home/surendralal/
13 /home/surendralal/
14 /home/surendralal/
15 /home/surendralal/
project \
0 notebooks/pyiron_potentialfit/day_1/first_steps/
1 notebooks/pyiron_potentialfit/day_1/first_steps/
2 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/
3 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/
4 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/
5 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/
6 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/
7 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/
8 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve/
9 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/
10 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/
11 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/
12 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/
13 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/
14 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/
15 notebooks/pyiron_potentialfit/day_1/first_steps/E_V_curve_DFT/
timestart timestop totalcputime \
0 2021-03-06 16:03:40.745470 2021-03-06 16:03:45.494803 4.0
1 2021-03-06 16:04:50.074383 2021-03-06 16:04:56.638128 6.0
2 2021-03-06 16:11:07.639631 2021-03-06 16:11:08.342303 0.0
3 2021-03-06 16:11:08.822636 2021-03-06 16:11:09.471384 0.0
4 2021-03-06 16:11:09.943131 2021-03-06 16:11:10.600866 0.0
5 2021-03-06 16:11:11.080672 2021-03-06 16:11:11.753735 0.0
6 2021-03-06 16:11:12.228943 2021-03-06 16:11:12.867039 0.0
7 2021-03-06 16:11:13.342478 2021-03-06 16:11:13.979644 0.0
8 2021-03-06 16:11:14.465906 2021-03-06 16:11:15.082557 0.0
9 2021-03-06 16:13:46.540931 2021-03-06 16:13:49.880940 3.0
10 2021-03-06 16:13:50.243705 2021-03-06 16:13:53.604881 3.0
11 2021-03-06 16:13:53.999279 2021-03-06 16:13:57.460432 3.0
12 2021-03-06 16:13:57.889876 2021-03-06 16:14:01.894945 4.0
13 2021-03-06 16:14:02.285518 2021-03-06 16:14:06.226798 3.0
14 2021-03-06 16:14:06.586832 2021-03-06 16:14:11.549242 4.0
15 2021-03-06 16:14:12.119382 2021-03-06 16:14:17.251638 5.0
computer hamilton hamversion parentid masterid
0 pyiron@cmdell17#1 Lammps 0.1 None None
1 pyiron@cmdell17#1 Sphinx 2.6.1 None None
2 pyiron@cmdell17#1 Lammps 0.1 None None
3 pyiron@cmdell17#1 Lammps 0.1 None None
4 pyiron@cmdell17#1 Lammps 0.1 None None
5 pyiron@cmdell17#1 Lammps 0.1 None None
6 pyiron@cmdell17#1 Lammps 0.1 None None
7 pyiron@cmdell17#1 Lammps 0.1 None None
8 pyiron@cmdell17#1 Lammps 0.1 None None
9 pyiron@cmdell17#1 Sphinx 2.6.1 None None
10 pyiron@cmdell17#1 Sphinx 2.6.1 None None
11 pyiron@cmdell17#1 Sphinx 2.6.1 None None
12 pyiron@cmdell17#1 Sphinx 2.6.1 None None
13 pyiron@cmdell17#1 Sphinx 2.6.1 None None
14 pyiron@cmdell17#1 Sphinx 2.6.1 None None
15 pyiron@cmdell17#1 Sphinx 2.6.1 None None
id status chemicalformula job subjob projectpath \
0 5 finished Cu108 lammps_job /lammps_job /home/pyiron/
project timestart timestop \
0 day_1/first_steps/ 2021-03-09 08:58:10.515085 2021-03-09 08:58:14.811278
totalcputime computer hamilton hamversion parentid \
0 4.0 pyiron@jupyter-janssen#1 Lammps 0.1 None
masterid
0 None
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## Analysing a calculation
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``` python
%%time
# Load the job
job_loaded = pr['lammps_job']
```
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``` python
job_loaded
```
%%%% Output: execute_result
{'groups': ['input', 'output'], 'nodes': ['HDF_VERSION', 'NAME', 'TYPE', 'VERSION', 'server', 'status']}
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``` python
job_loaded["output/generic"]
```
%%%% Output: execute_result
{'groups': [], 'nodes': ['cells', 'energy_pot', 'energy_tot', 'forces', 'indices', 'positions', 'pressures', 'steps', 'temperature', 'unwrapped_positions', 'velocities', 'volume']}
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``` python
final_struct = job_loaded.get_structure(iteration_step=-1)
final_struct.plot3d()
```
%%%% Output: display_data
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``` python
job_loaded.animate_structure()
```
%%%% Output: display_data
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``` python
temperatures = job_loaded['output/generic/temperature']
steps = job_loaded['output/generic/steps']
plt.plot(steps, temperatures)
......@@ -505,11 +427,11 @@
%%%% Output: display_data
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)
%% Cell type:code id:bearing-receipt tags:
%% Cell type:code id:happy-shopper tags:
``` python
pos = job_loaded['output/generic/positions']
x, y, z = [pos[:, :, i] for i in range(3)]
sel = np.abs(z) < 0.1
......@@ -522,56 +444,56 @@
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:markdown id:parallel-bunch tags:
%% Cell type:markdown id:leading-museum tags:
### **Running an atomistic calculation using DFT (with SPHInX)**
%% Cell type:code id:stone-feedback tags:
%% Cell type:code id:rough-shield tags:
``` python
job_sphinx = pr.create.job.Sphinx("sphinx_job")
job_sphinx.structure = Cu_unitcell_primitive
job_sphinx.set_exchange_correlation_functional("PBE")
job_sphinx.plane_wave_cutoff = 350
job_sphinx.calc_static()
job_sphinx.run()
```
%% Cell type:code id:tropical-complement tags:
%% Cell type:code id:eligible-pottery tags:
``` python
job_sphinx['output/generic/']
```
%%%% Output: execute_result
{'groups': ['dft'], 'nodes': ['cells', 'computation_time', 'energy_pot', 'energy_tot', 'forces', 'positions', 'volume']}
%% Cell type:code id:superior-cisco tags:
%% Cell type:code id:identical-delicious tags:
``` python
job_sphinx["output/generic/energy_tot"] # Energy for every ionic step
```
%%%% Output: execute_result
array([-5386.42735597])
array([-5386.42735492])
%% Cell type:code id:joined-prairie tags:
%% Cell type:code id:aggressive-chest tags:
``` python
job_sphinx['output/electronic_structure']
```
%%%% Output: execute_result
{'groups': ['dos'], 'nodes': ['TYPE', 'efermi', 'eig_matrix', 'k_points', 'k_weights', 'occ_matrix']}
%% Cell type:code id:flexible-horizontal tags:
%% Cell type:code id:forbidden-christmas tags:
``` python
eigenvalues = job_sphinx['output/electronic_structure/eig_matrix'].flatten()
occupancies = job_sphinx['output/electronic_structure/occ_matrix'].flatten()
......@@ -582,41 +504,41 @@
plt.ylabel('Occupancy');
```
%%%% Output: display_data
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)
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D4U5KlRyEJli2S/hSIiDig8gBJNwJnA5MldQJfBJpJDlgELAUuBNaQzPN0SbqtW9IC4E6gCFwfEY/t0bczM6uSb5QbqNqrmCbu7htHxMXDbA/g8kG2LSUJEDOzUVGUb5SrVO1cTG/Nao+Ie0e2HDOz2igUfB9EpWq7mD5T9rwVmA2sAM4d8YrMzGqg4DupB6i2i+ld5a8lTSVZhtTMbL+QzMVU6yrqS7VXMVXqBN44koWYmdWSB6kHqnYM4qvsulmtAJwEPJxTTWZmo67oG+UGqHpN6rLn3cCNEfGzHOoxM6uJgm+UG6DagPh/wKsR0QPJjKuSxkXE1vxKMzMbPR6kHqjaMYi7gbFlr8cCPxz5cszMaiOZzdUBUa7agGiNiFdKL9Ln4/Ipycxs9CWzuda6ivpSbUBsqVjx7RRgWz4lmZmNPs/mOlC1YxCfAv5DUmlW1SOA38+lIjOzGvBsrgNVe6PcckkzgNeTrNfwRETsHOYwM7N9hs8gBqp2RbnLgfER8WhEPAJMkPRH+ZZmZjZ6khvlal1Ffal2DOITEfFi6UVEbAI+kUtFZmY1kEy14TOIctUGREFS31KgkookK7+Zme0XfKPcQNUOUt8FfFfSIpIpNy4Dvp9bVWZmo6wgET6D6KfagPg8SZfSpSSD1HcBXx/uIElzgK+QrAx3XURcXbH9M8AHymo5HmiLiI2SngFeBnqA7ohor7JWM7Pd5i6mgYYMCElNwN+QLAe6jiQcpgJPk3RP9QxxbBG4FngbyeyvyyXdHhGrS/tExDXANen+7wL+JCI2lr3NORGxYQ++l5nZbkm6mGpdRX0ZbgziGuBg4LURcXJEzAKOASYBfz/MsbOBNRHxVETsAG4CLhpi/4uBG6sr28xsZBULuIupwnAB8U6SK5heLjWkzy8DLhzm2KNIzjpKOtO2ASSNA+YAN5c1B3CXpBWS5g/2IZLmS+qQ1NHV1TVMSWZm2TxIPdBwARGREanprK7D/SaV0TbYMe8CflbRvfSWiDgZuAC4fIh1sRdHRHtEtLe1tQ1TkplZtoLHIAYYLiBWS/pQZaOkPwCeGObYTpLxipIpwPpB9p1LRfdSRKxPfz4P3ErSZWVmlouihPOhv+GuYrocuEXSR4EVJGcAp5JM9/2eYY5dDhwr6Rjg1yQhMK9yJ0mTgLOAPyhrGw8UIuLl9Pn5wF9W9Y3MzPZAQbiLqcKQARERvwZOk3Qu8AaSbqM7IuLu4d44IrolLQDuJLnM9fqIeEzSpen2Remu7wHuiogtZYcfBtya3pvXBCyJCN93YWa5KXg9iAGqnazvR8CPdvfNI2IpsLSibVHF6xuAGyrangJO3N3PMzPbU0WvKDdAtVNtmJnt17zk6EAOCDMzSl1Mta6ivjggzMxIbpTzGUR/DggzMzwGkcUBYWYGyHdSD+CAMDMjmc211wHRjwPCzAxP953FAWFmBkjgE4j+HBBmZqSD1E6IfhwQZma4iymLA8LMjNKa1F40qJwDwsyMJCDA4xDlHBBmZiR3UoOn/C7ngDAzI5mLCTzdRjkHhJkZ5V1MDoiSXANC0hxJT0paI+mKjO1nS9os6aH08YVqjzUzG0nFNCDcxbRLVQsG7QlJReBa4G0k61Mvl3R7RKyu2PWnEfHOPTzWzGxE9HUxecrvPnmeQcwG1kTEUxGxA7gJuGgUjjUz221pPriLqUyeAXEUsK7sdWfaVunNkh6WdIekN+zmsUiaL6lDUkdXV9dI1G1mDaiYJoRvltslz4BQRlvlb/5B4OiIOBH4KnDbbhybNEYsjoj2iGhva2vb01rNrMH1DVJ7DKJPngHRCUwtez0FWF++Q0S8FBGvpM+XAs2SJldzrJnZSCoWfKNcpTwDYjlwrKRjJLUAc4Hby3eQdLiUxLak2Wk9L1RzrJnZSCqNQbiLaZfcrmKKiG5JC4A7gSJwfUQ8JunSdPsi4H3AZZK6gW3A3EgmQsk8Nq9azczcxTRQbgEBfd1GSyvaFpU9XwgsrPZYM7O89A1SOyD6+E5qMzN8J3UWB4SZGZ6LKYsDwsyM8qk2alxIHXFAmFlDW3TPWpat3dA33XdvBMvWbmDRPWtrW1gdcECYWUObOWUSC5as5OfPvQLAyl9tYsGSlcycMqnGldWeA8LMGtoZ0yezcN4sFt/7FABX3/EEC+fN4ozpk2tcWe05IMys4Z0xfTLnv+EwAM47/jCHQ8oBYWYNb9naDfxw9XMA/GD1cyxbu6HGFdUHB4SZNbRlazewYMlK/uKiZDLpD55+NAuWrHRI4IAwswa3qnMzC+fN4q3HJrNBHz6plYXzZrGqc3ONK6u9XKfaMDOrd5eeNR2A7d09ALz86k7OmD7Z4xD4DMLMDIAxTUVamgq8vL271qXUDQeEmVlq4pgmXnnVAVHigDAzS01sbeJlB0QfB4SZWWpCaxOvuIupjwPCzCw1wV1M/eQaEJLmSHpS0hpJV2Rs/4CkVeljmaQTy7Y9I+kRSQ9J6sizTjMzgImtzR6kLpPbZa6SisC1wNuATmC5pNsjYnXZbk8DZ0XEJkkXAIuB08q2nxMRvlvFzEbFxDFNvPzqzlqXUTfyPIOYDayJiKciYgdwE3BR+Q4RsSwiNqUv7wOm5FiPmdmQPAbRX54BcRSwrux1Z9o2mI8Bd5S9DuAuSSskzR/sIEnzJXVI6ujq6tqrgs2ssU1sTcYgwqvKAfneSa2MtszfuqRzSALizLLmt0TEekmHAj+Q9ERE3DvgDSMWk3RN0d7e7v9VzWyPTRjTTHdv8OrOXsa2FGtdTs3leQbRCUwtez0FWF+5k6SZwHXARRHxQqk9ItanP58HbiXpsjIzy82E1uTfzC9v9zgE5BsQy4FjJR0jqQWYC9xevoOk1wC3AB+MiJ+XtY+XNLH0HDgfeDTHWs3MmDgmCQhf6prIrYspIrolLQDuBIrA9RHxmKRL0+2LgC8AhwD/rGTB8O6IaAcOA25N25qAJRHx/bxqNTODZAwC8EB1KtfZXCNiKbC0om1R2fOPAx/POO4p4MTKdjOzPE1IzyA83UbCd1KbmaX6xiAcEIADwsyszwGtzYC7mEocEGZmqV1dTL6KCRwQZmZ9Sl1Mvoop4YAwM0s1Fwu0NhfcxZRyQJiZlZkwxjO6ljggzMzKeFW5XRwQZmZlkkWDPEgNDggzs34mesrvPg4IM7MyE8a4i6nEAWFmVmaCxyD6OCDMzMoc0NrsLqaUA8LMrMyEMckYhFeVc0CYmfUzobWJnt5g286eWpdScw4IM7MyEz3dRh8HhJlZatE9a3n2xW0AfXdTL1u7gUX3rK1lWTWT64JBkuYAXyFZUe66iLi6YrvS7RcCW4GPRMSD1RxrZjbSZk6ZxEe+sRyAby57hs5NW1n+zCZOnXYQv3xhC+868Ug+/Z2HOf7IiRx2QCurn32Jz86ZwWPrN3Pj/euYNnkcD617kZOmHkhvwFEHttIb8MDTG9m6o4dzZ7RxxyO/4bJzpvPfq56l66XtnPX6Nh54eiMARx8yjnt/voFDJ47hnBlt/O/ajUjQuXErLc1FLj/ndVz7ozVs2dGNBNMOGc/dnz6bj93wAD9ds4EJLU3MnHogN1wye0R+H8prIEZSEfg58Dagk2SN6osjYnXZPhcCnyQJiNOAr0TEadUcm6W9vT06Ojry+Dpm1iA+f9uj/Nt9v+QNRx7A6vUv0VwUTcVC36B1b8D27l7GNBUoKHm9o7uXc2e0cfcTXX3vM2vqJFau20xR0BP0/RysPUtp35LzZrTx4ye76C3b/3Vt41nTtaXv9ZXvmMEnfmt61d9X0op0qecB8uximg2siYinImIHcBNwUcU+FwHfisR9wIGSjqjyWDOzEffx3zoGgMfWv0SxADt6gq07eti2s5dtO3spCkQSEjt7etne3UsAdz/Rhcrep/SHvTIEHiprLzAwHIoa+B4ldz/RPxyAvQqH4eQZEEcB68ped6Zt1exTzbEASJovqUNSR1dXV9YuZmZV+/WL2zigtYnDDhhDdy8c0LqrJ/6Q8S1s3dnL6w+fyCHjW+juhbHNu/6MHnf4RF5/+MR+79dUSELg4PHNHHfYBEp/31ubC/QCxbK/wq3NBXoCWpt2/0/z7GkHjWg4QL4BoYy2yhOpwfap5tikMWJxRLRHRHtbW9tulmhmtsuytRtYsGQlnzzvdezsCc583WReerWbYgFaimLjlh28Z9aR/GrjVl7YsoPXHz6BbTt7aSqI1uYC6zZu5VcvbOn7oy+guxemHtTKxi07+eULW2ltLtBUEK/u7KVtQjM9vbv+4L26s5epB43l1e7e3a79gWc28a8/HdnB9DwDohOYWvZ6CrC+yn2qOdbMbESt6tzMZWe/lq/95CkuO/u1PNz5Ii1FUZAopiGw9JHfsHVHD2OaCvzqha2MaSrQ0xu8ZfohfV1RPb3J+EGQdBmt2/QqRSXdUscfPpHu3qAo6HplJ0X1/9fvuk3b+p7PmjqpX33nzWijUPHP59e1je97/tffe2JEQyLPgFgOHCvpGEktwFzg9op9bgc+pMTpwOaIeLbKY83MRtSlZ02npxcWzptFTy+8c+YR3PDR2bzvlCm8e9ZRfP0jp3LwuBbOndHGe08+iuMOn8g3LjmVP3/HDJ7esJVzZ7Rx8Phmzp3RxqRxLXzgtKm8/9SpTG8bz6EHtPKB06byyxe2cuU7ZvDGKZM4clJr3/bpbeM5d0YbTQVx5KRk383bupneNp4xRTGxtYlTjzmE8S1NFJR0TU1vG88PP302581oo6VJHDyumZ+teWHEfh+5XcUEfVcp/SPJparXR8RfS7oUICIWpZe5LgTmkFzmeklEdAx27HCf56uYzMx2z1BXMeUaEKPNAWFmtntqdZmrmZntwxwQZmaWyQFhZmaZHBBmZpZpvxqkltQF/LLWdVRhMrCh1kXsAdc9+vbV2l336Nqbuo+OiMy7jPergNhXSOoY7KqBeua6R9++WrvrHl151e0uJjMzy+SAMDOzTA6I2lhc6wL2kOsefftq7a57dOVSt8cgzMwsk88gzMwskwPCzMwyOSByJun3JD0mqVdSe8W2z0laI+lJSW8vaz9F0iPptn9KZ72tGUknSbpP0kPp6n2zy7Zlfod6IemTaW2PSfpSWXtd1w0g6c8khaTJZW11W7ekayQ9IWmVpFslHVi2rW7rBpA0J61tjaQral3PYCRNlfRjSY+n/5/+47T9YEk/kPSL9OdBI/KBEeFHjg/geOD1wE+A9rL2E4CHgTHAMcBaoJhuewB4M8lCU3cAF9T4O9xVqgG4EPjJcN+hHh7AOcAPgTHp60P3hbrTGqcCd5Lc+Dl5X6gbOB9oSp//HfB3+0jdxbSm1wItaa0n1LquQWo9Ajg5fT4R+Hn6+/0ScEXafkXpd7+3D59B5CwiHo+IJzM2XQTcFBHbI+JpYA0wW9IRwAER8b+R/K/9LeDdo1dxpgAOSJ9PYtfqfpnfoQb1DeYy4OqI2A4QEc+n7fVeN8A/AP+H/ouN1XXdEXFXRHSnL+8jWQkS6rxuklrWRMRTEbEDuImk5roTEc9GxIPp85eBx4GjSOr9ZrrbNxmhvxkOiNo5ClhX9rozbTsqfV7ZXkufAq6RtA74e+Bzaftg36FeHAf8lqT7Jd0j6dS0va7rlvQ7wK8j4uGKTXVdd4WPkpz9Qv3XXe/1ZZI0DZgF3A8cFslqnKQ/Dx2Jz2gaiTdpdJJ+CByesenKiPjPwQ7LaIsh2nM11HcAzgP+JCJulvR+4OvAb1OjWssNU3cTcBBwOnAq8F1Jr6X+6/5zku6aAYdltNVN3aX/r0u6EugGvl06LGP/erq+vt7rG0DSBOBm4FMR8VJew5QOiBEQEb+9B4d1kvQzl0wh6brpZNepeXl7rob6DpK+Bfxx+vI/gOvS54N9h1EzTN2XAbekXXUPSOolmdSsbuuW9CaSfvqH0//opwAPphcG1G3dJZI+DLwTOC/9vUMd1D2Meq+vH0nNJOHw7Yi4JW1+TtIREfFs2k39/ODvUD13MdXO7cBcSWMkHQMcCzyQnh6+LOn09OqlDwGDnYWMlvXAWenzc4FfpM8zv0MN6hvMbST1Iuk4kgHIDdRx3RHxSEQcGhHTImIayR+vkyPiN9Rx3ZBcCQR8FvidiNhatqmu6waWA8dKOkZSCzCXpOa6k/5N+DrweER8uWzT7cCH0+cfZqT+ZtR6VH5/fwDvIfmPfDvwHHBn2bYrSa6eeJKyK5WAduDRdNtC0jvea/gdzgRWkFzdcT9wynDfoR4eJIHw7+nv8kHg3H2h7orv8AzpVUz1XjfJ4PM64KH0sWhfqDut70KSK4LWknSX1bymQeo8k6T7a1XZ7/lC4BDgbpJ/vN0NHDwSn+epNszMLJO7mMzMLJMDwszMMjkgzMwskwPCzMwyOSDMzCyTb5QzG4akHuCRsqabIuLqWtVjNlp8mavZMCS9EhETRvg9m2LXxHZmdcldTGZ7SNIzkv5C0oPp+h0z0vbxkq6XtFzSSkkXpe0fkfQfkv4LuEvSOEnfTddP+E46qWC7pI9J+oeyz/mEpC8PUoZZbhwQZsMbmy6WVHr8ftm2DRFxMvA14M/StiuBH0XEqSRrUlwjaXy67c3AhyPiXOCPgE0RMRO4Cjgl3ecm4HfSOXcALgG+kdu3MxuExyDMhrctIk4aZFtpsrQVwHvT5+eT/IEvBUYr8Jr0+Q8iYmP6/EzgKwAR8aikVenzLZJ+BLxT0uNAc0SUj4GYjQoHhNne2Z7+7GHXf08CfjcqFoqSdBqwpbxpiPe9jmTa7yfw2YPViLuYzEbencAn05k3kTRrkP3+B3h/us8JwJtKGyLifpIpqOcBN+ZardkgHBBmw6scgxjuEtergGZglaRH09dZ/hloS7uWPksyQ+fmsu3fBX4WEZv2sn6zPeLLXM1qRFKRZHzhVUnTSaZpPi6SdZGR9N/AP0TE3bWs0xqXxyDMamcc8OP0aiUBl0XEDkkHkiyo87DDwWrJZxBmZpbJYxBmZpbJAWFmZpkcEGZmlskBYWZmmRwQZmaW6f8DFu8G6ezHMGkAAAAASUVORK5CYII=)
%% Cell type:markdown id:french-spare tags:
%% Cell type:markdown id:cloudy-korean tags:
## **Task 1: Energy volume curve for Al**
%% Cell type:code id:resistant-vegetation tags:
%% Cell type:code id:nearby-queen tags:
``` python
def get_volume(job):
return job["output/generic/volume"][-1]
```
%% Cell type:code id:bibliographic-chancellor tags:
%% Cell type:code id:boolean-spectrum tags:
``` python
def get_energy(job):
return job["output/generic/energy_tot"][-1]
```