{"cells":[{"metadata":{},"id":"physical-toolbox","cell_type":"markdown","source":"# Day 2 - Up-scaling pyiron workflows \nFollowing the introduction on the first day, the second day is focused on explaining how to up-scale existing simulation workflows. The tutorial is based on the molecular dynamics simulation code [LAMMPS](https://lammps.sandia.gov). Still the same protocols can also be executed with DFT codes like [VASP](https://www.vasp.at) and [S/PHI/nX](https://sxrepo.mpie.de). "},{"metadata":{},"id":"serial-omega","cell_type":"markdown","source":"## Energy volume curve\nFollowing the tutorial from the first day, the energy volume curve is calculated using a `for-loop` over 11 different strains. Start by importing `numpy` as well as the pyiron `Project` class and create a new project named `scaleup`. "},{"metadata":{"trusted":true},"id":"metric-conspiracy","cell_type":"code","source":"import numpy as np\nfrom pyiron import Project\npr = Project(\"scaleup\")","execution_count":1,"outputs":[]},{"metadata":{},"id":"amateur-samoa","cell_type":"markdown","source":"As a first step the lattice constant is optimized for the specific lattice constant of the selected interatomic potential using [LAMMPS](https://lammps.sandia.gov). By using auto-completion (tab-completion) only the `job_name` and the element in this case iron `Fe` have to be selected manually. A cubic cell is created by adding the option `cubic=True` when creating the bulk structure:"},{"metadata":{"trusted":true},"id":"speaking-promotion","cell_type":"code","source":"job_mini = pr.create.job.Lammps(job_name=\"job_mini\")\njob_mini.structure = pr.create.structure.ase.bulk(\"Fe\", cubic=True)","execution_count":2,"outputs":[]},{"metadata":{},"id":"enhanced-lewis","cell_type":"markdown","source":"From the list of available potentials in the [NIST](https://www.ctcms.nist.gov/potentials/) and [openKIM](https://openkim.org) potential databases, one potential is selected. Depending on the project it is commonly required to carefully test multiple interatomic potentials:"},{"metadata":{"trusted":true},"id":"attractive-responsibility","cell_type":"code","source":"potential = job_mini.list_potentials()[0]\npotential","execution_count":3,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"'1997--Ackland-G-J--Fe--LAMMPS--ipr1'"},"metadata":{}}]},{"metadata":{},"id":"forced-wesley","cell_type":"markdown","source":"Optimise the lattice constant of the cubic simulation cell using the `calc_minimize()` function with the additional parameter `pressue=0.0`. The potential is assinged to the job object and afterwards the calculation is executed using `run()`:"},{"metadata":{"trusted":true},"id":"recorded-warrior","cell_type":"code","source":"job_mini.calc_minimize(pressure=0)\njob_mini.potential = potential\njob_mini.run()","execution_count":4,"outputs":[{"output_type":"stream","text":"The job job_mini was saved and received the ID: 2\n","name":"stdout"}]},{"metadata":{},"id":"standing-excerpt","cell_type":"markdown","source":"The lattice constant is extracted from the final structure of the minimization calculation using the `get_structure()` function:  "},{"metadata":{"trusted":true},"id":"polished-sullivan","cell_type":"code","source":"alat_guess = job_mini.get_structure().cell[0,0]\nnp.round(alat_guess, 3)","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"2.866"},"metadata":{}}]},{"metadata":{},"id":"still-sheffield","cell_type":"markdown","source":"Based on the initial guess of the lattice constant 11 strains ranging from $90$% to $110$% are applied to calculate the energy volume curve. The calculation are named based on the applied strain and the required structures are again created using the `pr.create.structure.ase.bulk()` function. All calculation are executed directly inline. "},{"metadata":{"trusted":true},"id":"convinced-dallas","cell_type":"code","source":"for strain in np.linspace(0.9, 1.1, 11):\n    job_name = \"lmp_\" + str(np.round(strain,2)).replace(\".\", \"_\")\n    job = pr.create.job.Lammps(job_name=job_name)\n    print(alat_guess * strain**(1/3))\n    job.structure = pr.create.structure.ase.bulk(\"Fe\", a=alat_guess * strain**(1/3), )\n    job.potential = potential\n    job.run()","execution_count":6,"outputs":[{"output_type":"stream","text":"2.7675753208676444\nThe job lmp_0_9 was saved and received the ID: 3\n2.7879258702511898\nThe job lmp_0_92 was saved and received the ID: 4\n2.8079835805272397\nThe job lmp_0_94 was saved and received the ID: 5\n2.8277587643206537\nThe job lmp_0_96 was saved and received the ID: 6\n2.8472611651817745\nThe job lmp_0_98 was saved and received the ID: 7\n2.866499999891875\nThe job lmp_1_0 was saved and received the ID: 8\n2.885483996845223\nThe job lmp_1_02 was saved and received the ID: 9\n2.90422143094099\nThe job lmp_1_04 was saved and received the ID: 10\n2.9227201553630375\nThe job lmp_1_06 was saved and received the ID: 11\n2.9409876305783165\nThe job lmp_1_08 was saved and received the ID: 12\n2.9590309508440615\nThe job lmp_1_1 was saved and received the ID: 13\n","name":"stdout"}]},{"metadata":{},"id":"photographic-newsletter","cell_type":"markdown","source":"Overview of the existing calculation in the current project using `job_table()`: "},{"metadata":{"trusted":true},"id":"fundamental-parliament","cell_type":"code","source":"pr.job_table()","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"    id    status chemicalformula       job     subjob    projectpath  \\\n0    2  finished             Fe2  job_mini  /job_mini  /home/jovyan/   \n1    3  finished              Fe   lmp_0_9   /lmp_0_9  /home/jovyan/   \n2    4  finished              Fe  lmp_0_92  /lmp_0_92  /home/jovyan/   \n3    5  finished              Fe  lmp_0_94  /lmp_0_94  /home/jovyan/   \n4    6  finished              Fe  lmp_0_96  /lmp_0_96  /home/jovyan/   \n5    7  finished              Fe  lmp_0_98  /lmp_0_98  /home/jovyan/   \n6    8  finished              Fe   lmp_1_0   /lmp_1_0  /home/jovyan/   \n7    9  finished              Fe  lmp_1_02  /lmp_1_02  /home/jovyan/   \n8   10  finished              Fe  lmp_1_04  /lmp_1_04  /home/jovyan/   \n9   11  finished              Fe  lmp_1_06  /lmp_1_06  /home/jovyan/   \n10  12  finished              Fe  lmp_1_08  /lmp_1_08  /home/jovyan/   \n11  13  finished              Fe   lmp_1_1   /lmp_1_1  /home/jovyan/   \n\n     project                  timestart                   timestop  \\\n0   scaleup/ 2021-03-22 23:51:52.778780 2021-03-22 23:51:53.409059   \n1   scaleup/ 2021-03-22 23:51:54.545651 2021-03-22 23:51:55.084443   \n2   scaleup/ 2021-03-22 23:51:55.922362 2021-03-22 23:51:56.454064   \n3   scaleup/ 2021-03-22 23:51:57.354493 2021-03-22 23:51:57.921399   \n4   scaleup/ 2021-03-22 23:51:58.821766 2021-03-22 23:51:59.324438   \n5   scaleup/ 2021-03-22 23:52:00.450136 2021-03-22 23:52:01.021797   \n6   scaleup/ 2021-03-22 23:52:02.045621 2021-03-22 23:52:02.621343   \n7   scaleup/ 2021-03-22 23:52:03.866085 2021-03-22 23:52:04.411873   \n8   scaleup/ 2021-03-22 23:52:05.616177 2021-03-22 23:52:06.127510   \n9   scaleup/ 2021-03-22 23:52:07.527880 2021-03-22 23:52:08.085600   \n10  scaleup/ 2021-03-22 23:52:09.550049 2021-03-22 23:52:10.132919   \n11  scaleup/ 2021-03-22 23:52:11.452091 2021-03-22 23:52:12.000244   \n\n    totalcputime  \\\n0            0.0   \n1            0.0   \n2            0.0   \n3            0.0   \n4            0.0   \n5            0.0   \n6            0.0   \n7            0.0   \n8            0.0   \n9            0.0   \n10           0.0   \n11           0.0   \n\n                                                        computer hamilton  \\\n0   pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n1   pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n2   pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n3   pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n4   pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n5   pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n6   pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n7   pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n8   pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n9   pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n10  pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n11  pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   Lammps   \n\n   hamversion parentid masterid  \n0         0.1     None     None  \n1         0.1     None     None  \n2         0.1     None     None  \n3         0.1     None     None  \n4         0.1     None     None  \n5         0.1     None     None  \n6         0.1     None     None  \n7         0.1     None     None  \n8         0.1     None     None  \n9         0.1     None     None  \n10        0.1     None     None  \n11        0.1     None     None  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>status</th>\n      <th>chemicalformula</th>\n      <th>job</th>\n      <th>subjob</th>\n      <th>projectpath</th>\n      <th>project</th>\n      <th>timestart</th>\n      <th>timestop</th>\n      <th>totalcputime</th>\n      <th>computer</th>\n      <th>hamilton</th>\n      <th>hamversion</th>\n      <th>parentid</th>\n      <th>masterid</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>finished</td>\n      <td>Fe2</td>\n      <td>job_mini</td>\n      <td>/job_mini</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:51:52.778780</td>\n      <td>2021-03-22 23:51:53.409059</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#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>3</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_0_9</td>\n      <td>/lmp_0_9</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:51:54.545651</td>\n      <td>2021-03-22 23:51:55.084443</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1</td>\n      <td>Lammps</td>\n      <td>0.1</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_0_92</td>\n      <td>/lmp_0_92</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:51:55.922362</td>\n      <td>2021-03-22 23:51:56.454064</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#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>5</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_0_94</td>\n      <td>/lmp_0_94</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:51:57.354493</td>\n      <td>2021-03-22 23:51:57.921399</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#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>6</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_0_96</td>\n      <td>/lmp_0_96</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:51:58.821766</td>\n      <td>2021-03-22 23:51:59.324438</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#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>7</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_0_98</td>\n      <td>/lmp_0_98</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:52:00.450136</td>\n      <td>2021-03-22 23:52:01.021797</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#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>8</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_1_0</td>\n      <td>/lmp_1_0</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:52:02.045621</td>\n      <td>2021-03-22 23:52:02.621343</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#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>9</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_1_02</td>\n      <td>/lmp_1_02</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:52:03.866085</td>\n      <td>2021-03-22 23:52:04.411873</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#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>10</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_1_04</td>\n      <td>/lmp_1_04</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:52:05.616177</td>\n      <td>2021-03-22 23:52:06.127510</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#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>11</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_1_06</td>\n      <td>/lmp_1_06</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:52:07.527880</td>\n      <td>2021-03-22 23:52:08.085600</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#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>10</th>\n      <td>12</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_1_08</td>\n      <td>/lmp_1_08</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:52:09.550049</td>\n      <td>2021-03-22 23:52:10.132919</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#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>11</th>\n      <td>13</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_1_1</td>\n      <td>/lmp_1_1</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:52:11.452091</td>\n      <td>2021-03-22 23:52:12.000244</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1</td>\n      <td>Lammps</td>\n      <td>0.1</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"id":"clean-farming","cell_type":"markdown","source":"The simulation results are collected with a simple for loop by iterating over the jobs in the project using `iterjobs()` and appending the final volume of the calculation and the total energy to a set of lists:"},{"metadata":{"trusted":true},"id":"legendary-occasions","cell_type":"code","source":"vol_lst, eng_lst = [], []\nfor job in pr.iter_jobs(): \n    if \"lmp_\" in job.job_name:\n        vol_lst.append(job[\"output/generic/volume\"][-1])\n        eng_lst.append(job[\"output/generic/energy_tot\"][-1])","execution_count":8,"outputs":[]},{"metadata":{},"id":"brave-refrigerator","cell_type":"markdown","source":"Finally the simulation results of the results are visualised using `matplotlib` to plot the energy volume curve:"},{"metadata":{"trusted":true},"id":"genuine-edward","cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.plot(vol_lst, eng_lst)\nplt.xlabel(\"Volume\")\nplt.ylabel(\"Energy\")","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"Text(0, 0.5, 'Energy')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"id":"meaning-revelation","cell_type":"markdown","source":"## Murnaghan Class\nUsing `for-loops` to create large datasets is limited. A more scalable approach is using already existing classes and combine those like building blocks. For the example of calculating an energy volume curve the `Murnaghan` class implements the creation of different strained structures as well as the management of the related calculation. In particular these job classes or `GenericMasters` support various modes of communication with the HPC queuing system. For example submitting individual calculation or executing all calculation in a single job either in serial or in parallel. Still for this presentation all jobs are executed inline."},{"metadata":{},"id":"herbal-spice","cell_type":"markdown","source":"As a start a template job is created which uses the same interatomic potential as the previous calculation and the same already optimised crystal structure:"},{"metadata":{"trusted":true},"id":"satellite-kansas","cell_type":"code","source":"job_fe = pr.create.job.Lammps(job_name=\"job_fe\")\njob_fe.structure = job_mini.get_structure()\njob_fe.potential = potential","execution_count":10,"outputs":[]},{"metadata":{},"id":"extensive-saudi","cell_type":"markdown","source":"The same template job is used for multiple calculation in the following, therefore it is copied for the different applications using the `copy_template()` function:"},{"metadata":{"trusted":true},"id":"micro-surge","cell_type":"code","source":"job_murn = job_fe.copy_template(\n    new_job_name=\"job_murn\"\n)","execution_count":11,"outputs":[]},{"metadata":{},"id":"proper-touch","cell_type":"markdown","source":"From the newly created copy a new job is created of type `Murnaghan`. By creating the job from the previous job template the relation of the job and it is location on the filesystem is defined. The input of the `Murnaghan` class can again be edited using the edge notation:"},{"metadata":{"trusted":true},"id":"substantial-cabinet","cell_type":"code","source":"murn = job_murn.create_job(\n    job_type=pr.job_type.Murnaghan, \n    job_name=\"murn\"\n)\nmurn.input ","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"    Parameter       Value  \\\n0  num_points          11   \n1    fit_type  polynomial   \n2   fit_order           3   \n3   vol_range         0.1   \n\n                                                                               Comment  \n0                                                              number of sample points  \n1  ['polynomial', 'birch', 'birchmurnaghan', 'murnaghan', 'pouriertarantola', 'vinet']  \n2                                                             order of the fit polynom  \n3                           relative volume variation around volume defined by ref_ham  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Parameter</th>\n      <th>Value</th>\n      <th>Comment</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>num_points</td>\n      <td>11</td>\n      <td>number of sample points</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>fit_type</td>\n      <td>polynomial</td>\n      <td>['polynomial', 'birch', 'birchmurnaghan', 'murnaghan', 'pouriertarantola', 'vinet']</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>fit_order</td>\n      <td>3</td>\n      <td>order of the fit polynom</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>vol_range</td>\n      <td>0.1</td>\n      <td>relative volume variation around volume defined by ref_ham</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"id":"successful-cassette","cell_type":"markdown","source":"When the run method of the `Murnaghan` class is called, the individual calculation are started and after the successful completion the aggregated information is stored in the `Murnaghan` object:"},{"metadata":{"trusted":true},"id":"welsh-biodiversity","cell_type":"code","source":"murn.run()","execution_count":13,"outputs":[{"output_type":"stream","text":"The job murn was saved and received the ID: 14\nThe job strain_0_9 was saved and received the ID: 15\nThe job strain_0_92 was saved and received the ID: 16\nThe job strain_0_94 was saved and received the ID: 17\nThe job strain_0_96 was saved and received the ID: 18\nThe job strain_0_98 was saved and received the ID: 19\nThe job strain_1_0 was saved and received the ID: 20\nThe job strain_1_02 was saved and received the ID: 21\nThe job strain_1_04 was saved and received the ID: 22\nThe job strain_1_06 was saved and received the ID: 23\nThe job strain_1_08 was saved and received the ID: 24\nThe job strain_1_1 was saved and received the ID: 25\njob_id:  15 finished\njob_id:  16 finished\njob_id:  17 finished\njob_id:  18 finished\njob_id:  19 finished\njob_id:  20 finished\njob_id:  21 finished\njob_id:  22 finished\njob_id:  23 finished\njob_id:  24 finished\njob_id:  25 finished\n","name":"stdout"}]},{"metadata":{},"id":"upset-illustration","cell_type":"markdown","source":"In addition to the data aggregation methods the `Murnaghan` class also provides plotting methods for commonly used plots: "},{"metadata":{"trusted":true},"id":"determined-person","cell_type":"code","source":"murn.plot()","execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"id":"fatal-elizabeth","cell_type":"markdown","source":"## Phonopy interface \nFollowing the same principles also the `Phonopy` interface is implemented in pyiron. This enables using `Phonopy` in combination with all simulation codes implemented in pyiron. The required steps are more or less identical. Again starting by copying the job template using the `copy_template()` function. Starting with a bulk calculation as a reference:"},{"metadata":{"trusted":true},"id":"heated-responsibility","cell_type":"code","source":"job_bulk = job_fe.copy_template(\n    new_job_name=\"job_bulk\"\n)\njob_bulk.run()","execution_count":15,"outputs":[{"output_type":"stream","text":"The job job_bulk was saved and received the ID: 26\n","name":"stdout"}]},{"metadata":{},"id":"close-bailey","cell_type":"markdown","source":"Following the bluk calculation the `Phonopy` job is created by again copying the job template and creating a `Phonopy` job from the job object:"},{"metadata":{"trusted":true},"id":"czech-report","cell_type":"code","source":"job_phono = job_fe.copy_template(\n    new_job_name=\"job_phono\"\n)","execution_count":16,"outputs":[]},{"metadata":{},"id":"multiple-digest","cell_type":"markdown","source":"Again the input object of the `Phonopy` job provides access to the most commonly used input parameters: "},{"metadata":{"trusted":true},"id":"assisted-deviation","cell_type":"code","source":"phono = job_phono.create_job(\n    job_type=pr.job_type.PhonopyJob, \n    job_name=\"phono\"\n)\nphono.input ","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"           Parameter      Value  \\\n0  interaction_range  10.000000   \n1             factor  15.633302   \n2       displacement   0.010000   \n3           dos_mesh  20.000000   \n4   primitive_matrix        NaN   \n\n                                               Comment  \n0                       Minimal size of supercell, Ang  \n1  Frequency unit conversion factor (default for VASP)  \n2                              atoms displacement, Ang  \n3                        mesh size for DOS calculation  \n4                                                       ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Parameter</th>\n      <th>Value</th>\n      <th>Comment</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>interaction_range</td>\n      <td>10.000000</td>\n      <td>Minimal size of supercell, Ang</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>factor</td>\n      <td>15.633302</td>\n      <td>Frequency unit conversion factor (default for VASP)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>displacement</td>\n      <td>0.010000</td>\n      <td>atoms displacement, Ang</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>dos_mesh</td>\n      <td>20.000000</td>\n      <td>mesh size for DOS calculation</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>primitive_matrix</td>\n      <td>NaN</td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"id":"complimentary-advertiser","cell_type":"markdown","source":"Once more the job is executed using the `run()` function:"},{"metadata":{"trusted":true},"id":"regulated-arthritis","cell_type":"code","source":"phono.run()","execution_count":18,"outputs":[{"output_type":"stream","text":"The job phono was saved and received the ID: 27\nThe job job_phono_0 was saved and received the ID: 28\n","name":"stdout"}]},{"metadata":{},"id":"productive-cassette","cell_type":"markdown","source":"And the density of states can be plotted directly from the `Phonopy` job object:"},{"metadata":{"trusted":true},"id":"unknown-summer","cell_type":"code","source":"phono.plot_dos()","execution_count":19,"outputs":[{"output_type":"execute_result","execution_count":19,"data":{"text/plain":"<AxesSubplot:title={'center':'Phonon DOS vs Energy'}, xlabel='Frequency [THz]', ylabel='DOS'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"id":"waiting-questionnaire","cell_type":"markdown","source":"## Harmonic approximation\nIn addition to the calculation of the density of states `Phonopy` also enables the calculation of the free energy with the harmonic and quasi-harmonic approximation. Starting with the harmonic approximation, the free energy is calculated for temperatures ranging from $0$K to $800$K:"},{"metadata":{"trusted":true},"id":"naval-highland","cell_type":"code","source":"temperatures = np.linspace(0, 800, 41)\nbulk_thermal_properties = phono.get_thermal_properties(temperatures=temperatures)","execution_count":20,"outputs":[]},{"metadata":{},"id":"central-saint","cell_type":"markdown","source":"The free energy over temperature is plotted by combining the vibrational and the bulk energy:"},{"metadata":{"trusted":true},"id":"thick-repeat","cell_type":"code","source":"plt.plot(temperatures, job_bulk.output.energy_pot[-1] + bulk_thermal_properties.free_energies)\nplt.xlabel(\"Temperature [K]\")\nplt.ylabel(\"Free energy  ($U+F_{vib}$)  [eV]\");","execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"id":"fleet-target","cell_type":"markdown","source":"## Quasi-harmonic approximation\nThe major advantage of the `GenericMaster` classes is that they can not only be used to combine regular calculation, but also `GenericMaster` classes themselves. Like building blocks the different pyiron classes can be combined and nested. For the quasi-harmonic approximation the volume dependent free energy is calculated and the optimal volume for a given temperature is identifed by fitting. Starting once more by copying the template job using the `copy_template()` function: "},{"metadata":{"trusted":true},"id":"quarterly-vietnamese","cell_type":"code","source":"job_strain = job_fe.copy_template(\n    new_job_name=\"job_strain\"\n)","execution_count":22,"outputs":[]},{"metadata":{},"id":"behavioral-selling","cell_type":"markdown","source":"Aftewards the same template job is used to create both a `Murnaghan` job and a `Phonopy` job:"},{"metadata":{"trusted":true},"id":"informative-milton","cell_type":"code","source":"murn_strain = job_strain.create_job(\n    job_type=pr.job_type.Murnaghan,\n    job_name=\"murn_strain\"\n)\nphono_strain = job_strain.create_job(\n    job_type=pr.job_type.PhonopyJob,\n    job_name=\"phono_strain\"\n)","execution_count":23,"outputs":[]},{"metadata":{},"id":"therapeutic-slovak","cell_type":"markdown","source":"The `Phonopy` job is then used to create a `QuasiHarmonicJob` which calculates the free energy for each of the different volumes:"},{"metadata":{"trusted":true},"id":"damaged-sunset","cell_type":"code","source":"quasi_strain = phono_strain.create_job(\n    job_type=pr.job_type.QuasiHarmonicJob,\n    job_name=\"quasi_strain\"\n)","execution_count":24,"outputs":[]},{"metadata":{},"id":"egyptian-reason","cell_type":"markdown","source":"The input of the `QuasiHarmonicJob` is adjusted to range the temperature up to $800$K using $41$ steps:"},{"metadata":{"trusted":true},"id":"blond-tenant","cell_type":"code","source":"quasi_strain.input[\"temperature_end\"] = 800\nquasi_strain.input[\"temperature_steps\"] = 41","execution_count":25,"outputs":[]},{"metadata":{},"id":"based-europe","cell_type":"markdown","source":"After all calculation are setup they can be executed by again calling the `run()` commands on both the `Murnaghan` job and the `QuasiHarmonicJob` job:"},{"metadata":{"trusted":true},"id":"olive-undergraduate","cell_type":"code","source":"murn_strain.run()\nquasi_strain.run()","execution_count":26,"outputs":[{"output_type":"stream","text":"The job murn_strain was saved and received the ID: 29\nThe job strain_0_9 was saved and received the ID: 30\nThe job strain_0_92 was saved and received the ID: 31\nThe job strain_0_94 was saved and received the ID: 32\nThe job strain_0_96 was saved and received the ID: 33\nThe job strain_0_98 was saved and received the ID: 34\nThe job strain_1_0 was saved and received the ID: 35\nThe job strain_1_02 was saved and received the ID: 36\nThe job strain_1_04 was saved and received the ID: 37\nThe job strain_1_06 was saved and received the ID: 38\nThe job strain_1_08 was saved and received the ID: 39\nThe job strain_1_1 was saved and received the ID: 40\njob_id:  30 finished\njob_id:  31 finished\njob_id:  32 finished\njob_id:  33 finished\njob_id:  34 finished\njob_id:  35 finished\njob_id:  36 finished\njob_id:  37 finished\njob_id:  38 finished\njob_id:  39 finished\njob_id:  40 finished\nThe job quasi_strain was saved and received the ID: 41\nThe job strain_0_9 was saved and received the ID: 42\nThe job job_strain_0 was saved and received the ID: 43\nThe job strain_0_92 was saved and received the ID: 44\nThe job job_strain_0 was saved and received the ID: 45\nThe job strain_0_94 was saved and received the ID: 46\nThe job job_strain_0 was saved and received the ID: 47\nThe job strain_0_96 was saved and received the ID: 48\nThe job job_strain_0 was saved and received the ID: 49\nThe job strain_0_98 was saved and received the ID: 50\nThe job job_strain_0 was saved and received the ID: 51\nThe job strain_1_0 was saved and received the ID: 52\nThe job job_strain_0 was saved and received the ID: 53\nThe job strain_1_02 was saved and received the ID: 54\nThe job job_strain_0 was saved and received the ID: 55\nThe job strain_1_04 was saved and received the ID: 56\nThe job job_strain_0 was saved and received the ID: 57\nThe job strain_1_06 was saved and received the ID: 58\nThe job job_strain_0 was saved and received the ID: 59\nThe job strain_1_08 was saved and received the ID: 60\nThe job job_strain_0 was saved and received the ID: 61\nThe job strain_1_1 was saved and received the ID: 62\nThe job job_strain_0 was saved and received the ID: 63\n","name":"stdout"}]},{"metadata":{},"id":"sized-transsexual","cell_type":"markdown","source":"After the successful completion of all calculation the results can be visualised using `matplotlib` starting by creating a color map:"},{"metadata":{"trusted":true},"id":"patient-prize","cell_type":"code","source":"import matplotlib\ncmap = matplotlib.cm.get_cmap('coolwarm')","execution_count":27,"outputs":[]},{"metadata":{},"id":"civilian-warning","cell_type":"markdown","source":"The free energy is plotted over volume and temperature, starting at a low temperature of $0$K up to the $800$K:"},{"metadata":{"trusted":true},"id":"mounted-routine","cell_type":"code","source":"# Iterate over the the output of the QuasiHarmonicJob to plot the temperature dependent free energy over volume \nfor i, [t, free_energy, v] in enumerate(\n        zip(quasi_strain[\"output/temperatures\"].T, \n            quasi_strain[\"output/free_energy\"].T, \n            quasi_strain[\"output/volumes\"].T)):\n    color = cmap(i/len(quasi_strain[\"output/temperatures\"].T))\n    # Add the energy of the Murnaghan Job to the vibrational free energy\n    plt.plot(v, free_energy + murn_strain[\"output/energy\"], color=color)\n\n# Add labels to the plot     \nplt.xlabel(\"Volume\")\nplt.ylabel(\"Free Energy\")\n\n# Add a color bar to visualise the temperature dependence \ntemperatures = quasi_strain[\"output/temperatures\"]\nnormalize = matplotlib.colors.Normalize(vmin=temperatures.min(), vmax=temperatures.max())\nscalarmappaple = matplotlib.cm.ScalarMappable(norm=normalize, cmap=cmap)\nscalarmappaple.set_array(temperatures)\ncbar = plt.colorbar(scalarmappaple)\ncbar.set_label(\"Temperature\")","execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"id":"compact-excuse","cell_type":"markdown","source":"In addition to the free energy over temperature and volume, the optimised volume over temperature allows to quantify the volume expansion:"},{"metadata":{"trusted":true},"id":"democratic-friend","cell_type":"code","source":"# Use the optimise_volume() function of the QuasiHarmonicJob  \nv0_lst, free_eng_lst, entropy_lst, cv_lst = quasi_strain.optimise_volume(\n    # It requires the output energy of the energy volume curve as additional input \n    bulk_eng=murn_strain[\"output/energy\"]\n)\ntemperature_output_lst = quasi_strain[\"output/temperatures\"][0]","execution_count":29,"outputs":[]},{"metadata":{"trusted":true},"id":"floppy-rachel","cell_type":"code","source":"plt.plot(temperature_output_lst, v0_lst)\nplt.xlabel(\"Temperature\")\nplt.ylabel(\"Volume\")","execution_count":30,"outputs":[{"output_type":"execute_result","execution_count":30,"data":{"text/plain":"Text(0, 0.5, 'Volume')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAnH0lEQVR4nO3dd3xV9f3H8dfHEFbYEHbCBgVlRmS0uHArjqLiam21bgFHW0fVqtU6cVWtVruUqeD4qVWsg6pVkSQQRpiyN7LCCFmf3x/3Um/xJgTIzbnJfT8fjzxy77n33PsmwH3nrO/X3B0REZF9HRZ0ABERiU8qCBERiUoFISIiUakgREQkKhWEiIhEVSPoABWpWbNm3r59+6BjiIhUGZmZmZvcPTXaY9WqINq3b8+MGTOCjiEiUmWY2fLSHtMuJhERiUoFISIiUakgREQkKhWEiIhEpYIQEZGoVBAiIhKVCkJERKKKWUGYWZqZfWJmuWY218xG7fP4rWbmZtaslPX/YmYbzGxOrDKKiFR1M5Zt5oVpS2Ly2rHcgigCbnH3I4ABwPVm1h1C5QGcBKwoY/2/AafGMJ+ISJVVUFTCox/M54IXvmTc9BXsKiiq8PeI2ZXU7r4WWBu+nWdmuUAbYB7wBPBr4K0y1v+3mbWPVT4Rkapq8YY8Rk+cyZzV27kgoy13n9WDujUr/uO8UobaCH/Q9wG+NrNhwGp3n2VmlfH2IiLVQkmJ8/cvl/HQP+eTUqsGL1zWj1N6tIzZ+8W8IMysHjAZGE1ot9OdwMkV+PpXAVcBpKenV9TLiojElXXb8vnV67P4bNEmTji8OQ//pCep9WvF9D1jWhBmlkyoHMa6+xQzOwroAOzdemgLZJlZf3dfdzDv4e4vAi8CZGRkaIJtEal23slZw51vzKGgqIQHzz2Ki/qnURl7YGJWEBZK/zKQ6+5jANx9NtA84jnLgAx33xSrHCIiVdX2/ELueWsub2SvpndaI564sDcdmqVU2vvH8iymwcBlwAlmNjP8dXppTzaz1mb2XsT98cCXQDczW2VmV8Qwq4hIXPn62+847cnPeHvWGkYP7cLr1wys1HKA2J7F9DlQ5jaQu7ePuL0GOD3i/kWxyiYiEq8KikoY8+FCXvj3Eto1qcvr1wykT3rjQLJUqwmDRESqskXrQ6evzl2znYv6p/HbM7qTUiu4j2kVhIhIwNydf3y5nAffyyWlVg3+/NMMTureIuhYKggRkSBt2J7Pr17PYdrCjRzfLZWHh/ekef3aQccCVBAiIoGZOncdt02Zzc49Rdx/dg8uHdCuUk5fLS8VhIhIJdtVUMT978xj/PSV9GjdgKdG9KZz8/pBx/oBFYSISCWatXIroyfOZNl3O7nm2E7cfFJXataIz5kXVBAiIpWguMR5/tPFPPmvRaTWr8W4KwcwsFPToGOVSQUhIhJjKzfv4uZJM/lm2RbO7NmKB845ioZ1k4OOtV8qCBGRGHpr5mp++8YcHBhzQS/O7dMmrg5El0UFISISA5HjKPVr15gnL+xNWpO6Qcc6ICoIEZEKlrl8M6MmzGTN1t2MHtqFG47vTI2k+DwQXRYVhIhIBSkqLuGZjxfzzMeLaNO4Dq9dM5B+7ZoEHeugqSBERCrAys27GDUhm6wVWzmvTxvuPbsH9WvH/4HosqggREQO0ZvZq/ntm3Mwg6dG9Obs3m2CjlQhVBAiIgcpL7+Qu8MHojPaNebJEb1p27hqHYguiwpCROQgZC7fwuiJ2azZms9NQ7ty/fGdquSB6LKoIEREDkBxifPcJ4t58qNFtGpYm0lXD6jSB6LLooIQESmn1Vt3c9OEmUxftplhvVrz+3OPpEEVPxBdFhWEiEg5vJuzltun5FBc4lXuiuiDpYIQESnDroIi7n17HhNnrKRXWiOeHtGbdk1Tgo5VKVQQIiKlmLN6GyMnZLN0006uO64TN53UleRqdiC6LCoIEZF9lJQ4f/liKY+8v4DGKcmMveIYBnVuFnSsSqeCEBGJsDFvD7e+NotpCzcy9IgWPDK8J01SagYdKxAqCBGRsGkLN3LLpJnk5cfnHNGVTQUhIgmvoKiERz+Yz58/W0q3FvUZe+UAurWMvzmiK5sKQkQS2tJNOxk5PpvZq7dx6YB0fntGd2onJwUdKy6oIEQkYU3OXMVdb80hOekwXrisH6f0aBl0pLiighCRhLNjTxF3vTmHN7JX079DE568sDetG9UJOlbcUUGISELJWbWVG8dns3LzLm4a2pUbTuhM0mGJeyC6LCoIEUkIJSXOS59/yyPvL6B5/VpMvHogR7evnoPsVRQVhIhUexvz9nDLa7P498KNnNKjBQ//pCeN6ibmtQ0HQgUhItXa54s2MXriTLbnF/L7c47kkmPSE/rahgOhghCRaqmwuIQxHy7kT9OW0Cm1Hq9e2Z/DWzYIOlaVooIQkWpn5eZdjJyQTfaKrYw4Oo27z+pO3Zr6uDtQ+omJSLXybs5abpuSAw7PXNSHs3q1DjpSlaWCEJFqIb+wmPvemce4r1fQK60Rz4zoQ3rTukHHqtJUECJS5S3ekMcN47KZvy6Pq4d05NZTuiXUvA2xooIQkSrL3XltxirueXsudWsm8befH81x3ZoHHavaiFnFmlmamX1iZrlmNtfMRu3z+K1m5mYWdRYOMzvVzBaY2WIzuy1WOUWkasrLL2T0xJn8enIOfdIb8d6oH6scKlgstyCKgFvcPcvM6gOZZvahu88zszTgJGBFtBXNLAl4NvycVcA3Zva2u8+LYV4RqSJmr9rGjeOzWLF5F7ec1JXrjtdwGbEQsy0Id1/r7lnh23lALtAm/PATwK8BL2X1/sBid//W3QuACcDZscoqIlWDu/OXz5dy3vNfsKeohAlXDeTGE7uoHGKkUo5BmFl7oA/wtZkNA1a7+6wyrmZsA6yMuL8KOKaU174KuAogPT29oiKLSJzZuquAW1/L4V+56xl6RHMeHd6Lxgk6FWhliXlBmFk9YDIwmtBupzuBk/e3WpRlUbc23P1F4EWAjIyM0rZIRKQKy1y+mRvHZbNxxx7uPrM7Px/cXsNlVIKYFoSZJRMqh7HuPsXMjgI6AHu3HtoCWWbW393XRay6CkiLuN8WWBPLrCISf0pKnBf+/S2PTV1Am0Z1mHztIHq2bRR0rIQRs4KwUAO8DOS6+xgAd58NNI94zjIgw9037bP6N0AXM+sArAZGABfHKquIxJ9NO/Zw86TQCKxn9GzFH847iga1k4OOlVBiuQUxGLgMmG1mM8PL7nD396I92cxaAy+5++nuXmRmNwAfAEnAX9x9bgyzikgc+XLJd4yakM3W3YU8cO6RXNxfI7AGIWYF4e6fE/1YQuRz2kfcXgOcHnH/PSBqmYhI9VRc4jzz8SKe/mgR7Zul8Pdf9OeIVhqBNSi6klpE4sKG7fmMmjCTL7/9jvP6tOH+c44kpZY+ooKkn76IBO6zRRu5aeJMdu4p5tHhPTk/I23/K0nMqSBEJDBFxSU8+a9FPPvpYro0r8f4X/alS4v6QceSMBWEiARi3bZ8Rk7IZvrSzVyQ0ZZ7hx1JnZpJQceSCCoIEal0ny7YwM2TZpFfWMwTF/bi3D5tg44kUaggRKTSFIXniX7u0yUc3rI+f7y4L52b1ws6lpRCBSEilWLdtnxuHJ/FN8u2cFH/NO45qwe1k7VLKZ6pIEQk5iJ3KT15YW/O6dNm/ytJ4FQQIhIz++5SevaSvnRK1S6lqkIFISIx8b+7lNK556zu2qVUxaggRKTCTVsYuvBNu5SqNhWEiFSYouISnvjXQp79RLuUqgMVhIhUiPXb8xk5Ppuvl27mwow0fjeshy58q+JUECJyyD5ftInRE7PZuaeYMRf04ry+uvCtOlBBiMhBKy5xnv5oEU9/vIjOqRpLqbpRQYjIQdmQl8/oCTP5z5Lv+Enfttx/Tg/q1tRHSnWiv00ROWBfLvmOkROy2b67kEeG9+QCDc9dLakgRKTcSkqc56ct4fGpC2jfLIVXrujP4S0141t1pYIQkXLZvLOAmybOZNrCjQzr1ZoHzzuKeprxrVrT366I7Ffm8i3cMC6L73YU8PtzjuSSY9IxK3PKeakGVBAiUip35+XPl/LQP+fTulEdplw3iCPbNAw6llQSFYSIRLVtdyG/em0WU+et55QeLXhkeC8a1kkOOpZUIhWEiPzAnNXbuG5sFmu27uauM7vzi8HttUspAakgROS/3J2xX6/gvv+bR7N6NZl49UD6tWscdCwJiApCRADYuaeIO96YzVsz13Bs11SeuLA3TVJqBh1LAqSCEBEWrc/j2rFZfLtxB7ee3JXrjuvMYYdpl1KiU0GIJLg3sldxx5Q5pNRK4tUrjmFQ52ZBR5I4oYIQSVD5hcXc+3/zGD99Bf07NOGZi/rQokHtoGNJHNlvQVjo1IVLgI7ufp+ZpQMt3X16zNOJSEys+G4X143LZM7q7VxzbCduPbkrNZIOCzqWxJnybEE8B5QAJwD3AXnAZODoGOYSkRj5cN56bp40EwNe+mkGQ7u3CDqSxKnyFMQx7t7XzLIB3H2LmenUBpEqpqi4hEenLuCFad9yVJuGPHdJX9Ka1A06lsSx8hREoZklAQ5gZqmEtihEpIpYvz2fG8dlM33ZZi45Jp27zuxO7WRNByplK09BPA28ATQ3sweA4cBvY5pKRCrMf5ZsYuT40HSgT17Ym3P6tAk6klQR+y0Idx9rZpnAiYAB57h7bsyTicghiZy7oUOzFMb/coCmA5UDUt7TXNcDn4WfX8fM+rp7Vuxiicih2LorNHfDJws2clav1jx03lGkaO4GOUDlOc31fuByYAnh4xDh7yfELpaIHKycVVu59tUsNuTlc9/ZPbhsQDsNtCcHpTy/UlwAdHL3gliHEZGDFznQXmr9Wrx2zSB6pzUKOpZUYeW5MmYO0OhAX9jM0szsEzPLNbO5ZjYqvPx+M8sxs5lmNtXMWpey/igzmxNed/SBvr9IItlVUMRNE2fy2zfnMLBTU9658UcqBzlk5dmC+AOQbWZzgD17F7r7sP2sVwTc4u5ZZlYfyDSzD4FH3f0uADMbCdwNXBO5opkdCfwS6A8UAO+b2bvuvqicfy6RhLFk4w6ufTWTRRt2cPNJXbnheA20JxWjPAXxd+BhYDYHcP2Du68F1oZv55lZLtDG3edFPC2F749rRDoC+MrddwGY2TTgXOCR8r6/SCJ4J2cNv3k9h1rJSfzjF/35cZfUoCNJNVKegtjk7k8fypuYWXugD/B1+P4DwE+BbcDxUVaZAzxgZk2B3cDpwIxSXvsq4CqA9PT0Q4kpUmUUFJXw4Hu5/O0/y+ib3ohnL+lLq4Z1go4l1Yy5R/sFPuIJZmMI7Vp6m//dxVSu01zNrB4wDXjA3afs89jtQG13vyfKelcA1wM7gHnAbne/qaz3ysjI8BkzovaISLWxdtturh+bRdaKrfx8cHtuP+0IatbQQHtycMws090zoj1Wni2IPuHvAyKWles0VzNLJjSw39h9yyFsHPAu8IOCcPeXgZfDr/MgsKocWUWqtc8XbWLkhGz2FBbzx4v7cGbPqOd4iFSI8lxJHW0X0H6Fhwl/Gch19zERy7tEHGweBswvZf3m7r4hPLz4ecDAg8khUh2UlDjPfbqYxz9cSOfUejx/aT86N68XdCyp5spzodzd0Za7+337WXUwcBkw28xmhpfdAVxhZt0IHfBeTvgMpvDpri+5++nh504OH4MoBK539y37yypSHW3dVcDNk2bx8fwNDOvVmj/oqmipJOX5V7Yz4nZt4Exgv2MxufvnhMZu2td7pTx/DaGD0Xvv/7gc2USqtdmrtnHt2EzWb9dV0VL5yrOL6fHI+2b2GKED1iISI+7OhG9Wcs/bc2maUpNJVw+kT3rjoGNJgjmY7dS6QMeKDiIiIbsLirnrrTm8nrmKH3dpxlMj+tAkRXN0SeUrzzGI2Xx/MVsSkEpo6lERqWDLNu3k2rFZ5K7dzsgTuzDqxC4k6apoCUh5tiDOjLhdBKx396IY5RFJWFPnruOW12ZxmBl/vfxojj+8edCRJMGVWhBm1iR8M2+fhxqYGe6+OXaxRBJHUXEJj01dyJ+mLdFc0RJXytqCyCS0ayna9q2j4xAih2xj3h5uHJ/FV99u5uJj0rlbc0VLHCm1INy9Q2UGEUk0M5Zt5rqxWWzbXchj5/dieL+2QUcS+R/lOovJzIYBQ8J3P3X3d2IXSaR6c3f+8sUy/vBeLm0a1+FvP+9P99YNgo4l8gPlOYvpIeBoYGx40SgzG+zut8c0mUg1tGNPEb+ZnMO7OWs5qXsLHju/Fw3rJAcdSySq8mxBnA70dvcSADP7O5ANqCBEDsCi9Xlc82omSzft5LbTDufqIR11VbTEtfJeKNcI2HvWUsPYRBGpvt6etYbbJudQt2YSY68cwMBOTYOOJLJfZZ3m+kdgPPAgkGVmnxI6o2kI2noQKZfIiX0y2jXm2Uv60qJB7aBjiZRLWVsQi4DHgFbAVGAlMAv4jbuvq4RsIlXaum35XD8ui8zlW/jF4A7cfvrhJCdpYh+pOso6zfUp4CkzaweMCH9dDIwzswkRczqIyD7+s3gTN47PJr+wmGcv7ssZPVsFHUnkgO331xl3X+7uD7t7H0IFcR6lTPIjkuj2Tuxz6ctf0zilJm/dMFjlIFVWeU5zTQZOJbQFcSKh+aXvjXEukSpn2+5Cbpk0i3/lrufMnq14+Cc9NbGPVGllHaQ+CbgIOAOYDkwArnL3naWtI5Ko5q3ZzrVjM1m9ZTf3nNWdywe11ymsUuWV9evNHcA44FYNzCdSusmZq7jjjdk0qpvMxKsH0K9dk/2vJFIFlHWQ+vjKDCJS1eQXFnPfO/MY9/UKBnZsytMX9SG1fq2gY4lUGO0gFTkIq7bs4rqxWeSs2sa1x3XilpO6UkOnsEo1o4IQOUCfLtjA6IkzKS52XrysHyf3aBl0JJGYUEGIlFNJifP0x4t46qNFdGtRnz9d2o/2zVKCjiUSMyoIkXLYsrOA0RNnMm3hRs7r24YHzjmKOjU1sY9UbyoIkf3IWbWVa1/NYmPeHh489ygu6p+mU1glIaggRErh7oybvoJ7355Hav1avHbNQHqlNQo6lkilUUGIRLG7oJg735zNlKzVDOmaylMX9qZxSs2gY4lUKhWEyD6WbtrJta9msmB9HqOHduHGE7qQdJh2KUniUUGIRPhg7jpunTSLpCTjr5cfzXHdmgcdSSQwKggRoKi4hEenLuCFad/Ss21DnrukL20b1w06lkigVBCS8Dbk5TNyfDZffbuZS45J5+6zulOrhk5hFVFBSEKbvnQzN4zLYnt+IY+f34uf9GsbdCSRuKGCkITk7rz02VIeen8+6U3q8o8r+nN4ywZBxxKJKyoISTh5+YX86rUc3p+7jlN7tOSR83vSoHZy0LFE4o4KQhLK/HXbufbVLFZs3sWdpx/BlT/uoKuiRUqhgpCEMSUrNLFPg9rJjP/lAPp30MQ+ImVRQUi1FzmxzzEdmvDMxX1oXr920LFE4p4KQqq1lZtDE/vMXr2Nq4/tyK9O7qaJfUTKKWYFYWZpwD+AlkAJ8KK7P2Vm9wNnh5dtAC539zVR1r8JuBJwYDbwc3fPj1VeqX7+NW89N0+aiQN//mkGJ3VvEXQkkSollr9KFQG3uPsRwADgejPrDjzq7j3dvTfwDnD3viuaWRtgJJDh7kcCScCIGGaVaqSouISH35/Plf+YQVqTurx7449VDiIHIWZbEO6+Flgbvp1nZrlAG3efF/G0FEJbCKVlq2NmhUBd4AdbGSL7irwq+qL+adxzVg9qJ+uqaJGDUSnHIMysPdAH+Dp8/wHgp8A24Ph9n+/uq83sMWAFsBuY6u5TS3ntq4CrANLT02MRX6qIyKuiHzu/F8N1VbTIIYn50TozqwdMBka7+3YAd7/T3dOAscANUdZpTOg4RQegNZBiZpdGe313f9HdM9w9IzU1NVZ/DIljJSXOn6Yt4aI/f0VKrRq8ef1glYNIBYhpQZhZMqFyGOvuU6I8ZRzwkyjLhwJL3X2juxcCU4BBsUsqVdXWXQX88h8zeOif8zmlRwvevmGwhswQqSCxPIvJgJeBXHcfE7G8i7svCt8dBsyPsvoKYICZ1SW0i+lEYEasskrVNGvlVq4bm8WGvHx+d1Z3fjaova6KFqlAsTwGMRi4DJhtZjPDy+4ArjCzboROc10OXANgZq2Bl9z9dHf/2sxeB7IInQ2VDbwYw6xShbg7r3y1nN+/k0tq/VpMunogfdIbBx1LpNox99JOIqp6MjIyfMYMbWhUZzv2FHH7lNn836w1HN8tlTEXaK5okUNhZpnunhHtMV1JLVXGgnV5XDs2k2WbdvKrU7px7bGdOExzRYvEjApCqoRJM1Zy91tzqFcrmbFXDmBgp6ZBRxKp9lQQEtd2FRRx15tzmZy1ikGdmvLkiN4aaE+kkqggJG4tWp/HdWOzWLxxB6NO7MLIE7uQpF1KIpVGBSFxaXLmKn775hxSaiXxyi+O4UddmgUdSSThqCAkruwuKOaet+cwacYqBnRswtMj+tC8gXYpiQRBBSFxY/GGPK4fm83CDXnceEJnRp3YRXM3iARIBSFx4fXMVdz15hzq1kzibz/vz7FdNa6WSNBUEBKonXuKuOvNOUzJXs3AjqGzlFpol5JIXFBBSGDmrdnODeOzWLppJ6OHduHGE3SWkkg8UUFIpXN3xn69gvvemUejOsmMvfIYBnXSWUoi8UYFIZVqe34ht0+Zzbs5axnSNZUxF/SiWb1aQccSkShUEFJpsldsYeSEbNZszec3px7O1UM6aiwlkTimgpCYKylx/vTvJYyZupAWDWoz6eoB9GvXJOhYIrIfKgiJqfXb87l50ky+WPwdZxzVigfPO4qGdZKDjiUi5aCCkJj5eP56bn0th10FRTz8k6O4ICNNM76JVCEqCKlwe4qKeeif8/nrF8s4vGV9/njxADo3rx90LBE5QCoIqVCLN+QxcvxM5q3dzuWD2nPbaYdTOzkp6FgichBUEFIh3J1Xv1rOA+/lUic5iZd+msHQ7i2CjiUih0AFIYdsY94efjM5h4/nb2BI11QeG95TI7CKVAMqCDkkH89fz69fz2F7fhH3nNWdnw1sr2sbRKoJFYQclN0FxTz4Xi6vfLWcw1vWZ+yVA+jWUgeiRaoTFYQcsDmrtzFqQjZLNu7kyh914FendqNWDR2IFqluVBBSbkXFJTz/6RKe+mgRTevV5NUrNBWoSHWmgpByWbwhj1smzWLWqm0M69Wa+87uQaO6NYOOJSIxpIKQMhWXOH/9YimPfLCAlJpJPHtxX87o2SroWCJSCVQQUqoV3+3i1tdmMX3ZZoYe0YI/nHcUqfU1NLdIolBByA+4O+Omr+CBd3NJMuPx83txXt82GkdJJMGoIOR/rPhuF7e/kcMXi7/jR52b8cjwnrRuVCfoWCISABWEAKEzlP76xTIe/3ABNQ47jN+fcySXHJOurQaRBKaCEHLXbuc3k3PIWbWNoUc05/5zjqRVQ201iCQ6FUQCyy8s5o8fL+ZP05bQsE4yz1zUhzN7ttJWg4gAKoiE9c2yzfxmcg7fbtzJeX3bcNcZ3WmcousaROR7KogEs2nHHh7+53xey1xFm0Z1+Psv+nNs19SgY4lIHFJBJIii4hJe/Wo5j3+4kN0FxVw9pCMjT+xCSi39ExCR6PTpkABmLNvMXW/NJXftdn7UuRm/G9aDzs3rBR1LROKcCqIa25CXz0P/nM+UrNW0blib5y7py2lHttRBaBEpl5gVhJmlAf8AWgIlwIvu/pSZ3Q+cHV62Abjc3dfss243YGLEoo7A3e7+ZKzyVif5hcW88uVynv5oEflFxVx3XCduOKEzdWvq9wERKb9YfmIUAbe4e5aZ1QcyzexD4FF3vwvAzEYCdwPXRK7o7guA3uHnJAGrgTdimLVaKC5xJmet4skPF7JmWz7HdUvl7jO70zFVu5NE5MDFrCDcfS2wNnw7z8xygTbuPi/iaSmA7+elTgSWuPvy2CSt+tydD+et59EPFrBoww56pTXisQt6MaiT5moQkYNXKfsczKw90Af4Onz/AeCnwDbg+P2sPgIYX8ZrXwVcBZCenl4BaauW6Us38/D788lcvoWOqSk8f0lfTtVxBhGpAOa+v1/gD/ENzOoB04AH3H3KPo/dDtR293tKWbcmsAbo4e7r9/deGRkZPmPGjApIHf8yl2/h2U8W8/H8DbRsUJvRQ7swvF9baiQdFnQ0EalCzCzT3TOiPRbTLQgzSwYmA2P3LYewccC7QNSCAE4DsspTDomguCS0K+nPn31L5vItNKyTzG2nHc7lg9pTO1lzQotIxYrlWUwGvAzkuvuYiOVd3H1R+O4wYH4ZL3MRZexeShS7C4p5PXMlL3++lGXf7SKtSR3uHdaD8zPa6swkEYmZWH66DAYuA2ab2czwsjuAK8KnsZYAywmfwWRmrYGX3P308P26wEnA1THMGNc25u3hlS+X8cpXy9myq5DeaY147tTDOaVHS5IO0zEGEYmtWJ7F9DkQ7VPsvVKevwY4PeL+LqBpbNLFr+35hUydu563Z63hi8WbKHHnpCNacNWQjvRr11gHn0Wk0mj/RBzILyzmo9wNvD1rNZ8s2EhBUQltG9fhqiEdOb9fW13HICKBUEEEwN1ZuXk3WSu2MG3hRqbOXcfOgmJS69fi4v7pDOvdmj5pjbS1ICKBUkFUgvzCYnJWbSNz+RayVmwhe8UWNu0oAKBhnWTO6tWaYb1ac0zHpjq2ICJxQwVxiEpKnG27C1mfl8/67XtYvy2f9dvz/3t/zdbdLFiXR1FJ6HqT9k3rMqRLKn3bNaZvemO6tayvUhCRuKSCAM585jPyC0uIvGgw8vJBdygsLgl/+f/cLi6JfqFh47rJtGhQm+YNavPLIan0TW9M3/RGNK1XK8Z/GhGRiqGCADqn1qOwOPxBH/HL/N6bZkZyklEz6TCS//tl/71dr3YNWjaoTYsGtWjRoDap9WvpwjURqfJUEMCTI/oEHUFEJO5o4B4REYlKBSEiIlGpIEREJCoVhIiIRKWCEBGRqFQQIiISlQpCRESiUkGIiEhUMZ+TujKZ2UZCkxAdjGbApgqMU1GU68Ao14FRrgNTHXO1c/fUaA9Uq4I4FGY2o7SJu4OkXAdGuQ6Mch2YRMulXUwiIhKVCkJERKJSQXzvxaADlEK5DoxyHRjlOjAJlUvHIEREJCptQYiISFQqCBERiSrhC8LMTjWzBWa22Mxuq+T3/ouZbTCzORHLmpjZh2a2KPy9ccRjt4dzLjCzU2KYK83MPjGzXDOba2aj4iGbmdU2s+lmNiuc6954yBXxXklmlm1m78RLLjNbZmazzWymmc2Io1yNzOx1M5sf/nc2MOhcZtYt/HPa+7XdzEYHnSv8PjeF/83PMbPx4f8Lsc/l7gn7BSQBS4COQE1gFtC9Et9/CNAXmBOx7BHgtvDt24CHw7e7h/PVAjqEcyfFKFcroG/4dn1gYfj9A81GaBbYeuHbycDXwICgc0XkuxkYB7wTR3+Xy4Bm+yyLh1x/B64M364JNIqHXBH5koB1QLugcwFtgKVAnfD9ScDllZErZj/gqvAFDAQ+iLh/O3B7JWdoz/8WxAKgVfh2K2BBtGzAB8DASsr4FnBSPGUD6gJZwDHxkAtoC3wEnMD3BREPuZbxw4IINBfQIPyBZ/GUa58sJwNfxEMuQgWxEmhCaJrod8L5Yp4r0Xcx7f3B77UqvCxILdx9LUD4e/Pw8kCymll7oA+h39YDzxbejTMT2AB86O5xkQt4Evg1UBKxLB5yOTDVzDLN7Ko4ydUR2Aj8NbxL7iUzS4mDXJFGAOPDtwPN5e6rgceAFcBaYJu7T62MXIleEBZlWbye91vpWc2sHjAZGO3u28t6apRlMcnm7sXu3pvQb+z9zezIoHOZ2ZnABnfPLO8qUZbF6u9ysLv3BU4DrjezIWU8t7Jy1SC0a/V5d+8D7CS0iyToXKE3M6sJDANe299ToyyLxb+vxsDZhHYXtQZSzOzSysiV6AWxCkiLuN8WWBNQlr3Wm1krgPD3DeHllZrVzJIJlcNYd58ST9kA3H0r8ClwahzkGgwMM7NlwATgBDN7NQ5y4e5rwt83AG8A/eMg1ypgVXjrD+B1QoURdK69TgOy3H19+H7QuYYCS919o7sXAlOAQZWRK9EL4hugi5l1CP/WMAJ4O+BMbwM/C9/+GaH9/3uXjzCzWmbWAegCTI9FADMz4GUg193HxEs2M0s1s0bh23UI/ceZH3Qud7/d3du6e3tC/4Y+dvdLg85lZilmVn/vbUL7recEncvd1wErzaxbeNGJwLygc0W4iO93L+19/yBzrQAGmFnd8P/NE4HcSskVywM9VeELOJ3QWTpLgDsr+b3HE9qnWEio9a8AmhI62Lko/L1JxPPvDOdcAJwWw1w/IrRJmgPMDH+dHnQ2oCeQHc41B7g7vDzwn1nE+x3H9wepg/55dSR0NsssYO7ef99B5wq/T29gRvjv8k2gcZzkqgt8BzSMWBYPue4l9MvQHOAVQmcoxTyXhtoQEZGoEn0Xk4iIlEIFISIiUakgREQkKhWEiIhEpYIQEZGoagQdQKQymNneUwIBWgLFhIZ7AOjv7gWBBIvCzI4DCtz9PwFHkQSngpCE4O7fETr3HjP7HbDD3R8LKo+Z1XD3olIePg7YAZS7IMwsyd2LKyKbyF7axSQJy8z6mdm08EB2H0QMW/CpmT1hZv8Oz1VwtJlNCY+7//vwc9qH5zL4u5nlWGhug7rleN0HzWwaMMrMzjKzr8MD1v3LzFqEB0e8BrjJQnMS/NjM/mZmwyNy7wh/P85C83aMA2aHBzJ81My+CWe6ulJ/oFLtqCAkURnwDDDc3fsBfwEeiHi8wN2HAH8iNITB9cCRwOXh3VUA3YAX3b0nsB24LjyGVVmv28jdj3X3x4HPgQEeGrBuAvBrd18Wfs8n3L23u3+2nz9Hf0JXSHcndCX+Nnc/Gjga+GV4qAWRg6JdTJKoahH6wP8wNLwNSYSGPdlr75hcs4G5Hh5W2cy+JTQQ2lZgpbt/EX7eq8BI4P39vO7EiNttgYnhLYyahOZIOFDT3X3veicDPSO2NhoSGofnYF5XRAUhCcsIffAPLOXxPeHvJRG3997f+/9m33FqvByvuzPi9jPAGHd/O3xg+nelrFNEeGs/PFhbzVJez4Ab3f2DUl5H5IBoF5Mkqj1AqpkNhNDw5mbW4wBfI33v+oRGAP2c0OBo5X3dhsDq8O2fRSzPIzTV617LgH7h22cTmm41mg+Aa8O7uTCzruFRXEUOigpCElUJMBx42MxmERqxdtABvkYu8DMzyyE0HeTz4dNly/u6vwNeM7PPgE0Ry/8POHfvQWrgz8CxZjad0BSrO3/wSiEvERo2O8vM5gAvoL0Ecgg0mqvIQQifbfSOu5c1o51IlaYtCBERiUpbECIiEpW2IEREJCoVhIiIRKWCEBGRqFQQIiISlQpCRESi+n+GjKaSeMmg6AAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"id":"collaborative-velvet","cell_type":"markdown","source":"Finally based on the volume expansion over temperature the quasi-harmonic free energy is calculated: "},{"metadata":{"trusted":true},"id":"personalized-strategy","cell_type":"code","source":"plt.plot(temperature_output_lst, free_eng_lst)\nplt.xlabel(\"Temperature\")\nplt.ylabel(\"Free Energy\")","execution_count":31,"outputs":[{"output_type":"execute_result","execution_count":31,"data":{"text/plain":"Text(0, 0.5, 'Free Energy')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"id":"competitive-energy","cell_type":"markdown","source":"This example demonstrates how pyiron can be used to calculate free energies. "},{"metadata":{},"id":"seeing-montgomery","cell_type":"markdown","source":"## Scriptjob\nFrom a more technical perspective the `GenericMaster` classes are only one kind of tool integrated in pyiron to up-scale existing simulation protocols. While they provide a lot of flexibility like nesting multiple levels of `GenericMaster` objects, the development of `GenericMaster` classes requires a general understanding of pyiron. A more flexible way to up-scale an existing workflow is using `Scriptjob` objects which can execute existing python scripts and also existing jupyter notebooks. So once a new simulation workflow is implemented in a jupyter notebook this jupyter notebook can be used as an input for a `Scriptjob` to loop over a control parameter like looping over existing interatomic potentials. In this example we create a simple `Scriptjob` which just contains a python script:"},{"metadata":{"trusted":true},"id":"charitable-skirt","cell_type":"code","source":"python_script = \"\"\"\\\nfrom pyiron import Project\npr = Project(\"script\")\njob = pr.create.job.Lammps(\"lmp\")\njob.structure = pr.create.structure.ase.bulk(\"Fe\")\njob.run()\n\"\"\"","execution_count":32,"outputs":[]},{"metadata":{},"id":"offshore-conducting","cell_type":"markdown","source":"The python script which creates a single LAMMPS calculation is written to a python file named `script.py`. "},{"metadata":{"trusted":true},"id":"future-penetration","cell_type":"code","source":"with open(\"script.py\", \"w\") as f:\n    f.writelines(python_script)","execution_count":33,"outputs":[]},{"metadata":{},"id":"framed-demonstration","cell_type":"markdown","source":"Afterwards a `ScriptJob` object is created from the project object and the python file `script.py` is attached as `script_path` before the job is executed using the `run()` function. Again the `ScriptJob` object behaves like any other job object in pyiron:"},{"metadata":{"trusted":true},"id":"orange-cedar","cell_type":"code","source":"job_script = pr.create.job.ScriptJob(\"script\")\njob_script.script_path = \"script.py\"\njob_script.run()","execution_count":34,"outputs":[{"output_type":"stream","text":"The job script was saved and received the ID: 64\n","name":"stdout"}]},{"metadata":{},"id":"norwegian-american","cell_type":"markdown","source":"By looking at the job table it is visible that not only the `ScriptJob` itself but also the job executed from the python script named `lmp` is listed in the project:"},{"metadata":{"trusted":true},"id":"packed-trouble","cell_type":"code","source":"pr.job_table()","execution_count":35,"outputs":[{"output_type":"execute_result","execution_count":35,"data":{"text/plain":"    id    status chemicalformula           job         subjob    projectpath  \\\n0    2  finished             Fe2      job_mini      /job_mini  /home/jovyan/   \n1    3  finished              Fe       lmp_0_9       /lmp_0_9  /home/jovyan/   \n2    4  finished              Fe      lmp_0_92      /lmp_0_92  /home/jovyan/   \n3    5  finished              Fe      lmp_0_94      /lmp_0_94  /home/jovyan/   \n4    6  finished              Fe      lmp_0_96      /lmp_0_96  /home/jovyan/   \n..  ..       ...             ...           ...            ...            ...   \n59  61  finished           Fe128  job_strain_0  /job_strain_0  /home/jovyan/   \n60  62  finished             Fe2    strain_1_1    /strain_1_1  /home/jovyan/   \n61  63  finished           Fe128  job_strain_0  /job_strain_0  /home/jovyan/   \n62  64  finished            None        script        /script  /home/jovyan/   \n63  65  finished              Fe           lmp           /lmp  /home/jovyan/   \n\n                                        project                  timestart  \\\n0                                      scaleup/ 2021-03-22 23:51:52.778780   \n1                                      scaleup/ 2021-03-22 23:51:54.545651   \n2                                      scaleup/ 2021-03-22 23:51:55.922362   \n3                                      scaleup/ 2021-03-22 23:51:57.354493   \n4                                      scaleup/ 2021-03-22 23:51:58.821766   \n..                                          ...                        ...   \n59  scaleup/quasi_strain_hdf5/strain_1_08_hdf5/ 2021-03-22 23:54:24.027821   \n60                   scaleup/quasi_strain_hdf5/ 2021-03-22 23:54:28.338953   \n61   scaleup/quasi_strain_hdf5/strain_1_1_hdf5/ 2021-03-22 23:54:31.821842   \n62                                     scaleup/ 2021-03-22 23:54:50.762741   \n63           scaleup/script_hdf5/script/script/ 2021-03-22 23:54:53.820840   \n\n                     timestop  totalcputime  \\\n0  2021-03-22 23:51:53.409059           0.0   \n1  2021-03-22 23:51:55.084443           0.0   \n2  2021-03-22 23:51:56.454064           0.0   \n3  2021-03-22 23:51:57.921399           0.0   \n4  2021-03-22 23:51:59.324438           0.0   \n..                        ...           ...   \n59 2021-03-22 23:54:24.729845           0.0   \n60 2021-03-22 23:54:34.997655           6.0   \n61 2021-03-22 23:54:32.536800           0.0   \n62 2021-03-22 23:54:55.087347           4.0   \n63 2021-03-22 23:54:54.309320           0.0   \n\n                                                            computer  \\\n0       pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   \n1       pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   \n2       pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   \n3       pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   \n4       pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   \n..                                                               ...   \n59      pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   \n60  pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1#1/1   \n61      pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   \n62      pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   \n63      pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1   \n\n      hamilton hamversion parentid  masterid  \n0       Lammps        0.1     None       NaN  \n1       Lammps        0.1     None       NaN  \n2       Lammps        0.1     None       NaN  \n3       Lammps        0.1     None       NaN  \n4       Lammps        0.1     None       NaN  \n..         ...        ...      ...       ...  \n59      Lammps        0.1     None      60.0  \n60  PhonopyJob      0.0.1     None      41.0  \n61      Lammps        0.1     None      62.0  \n62      Script        0.1     None       NaN  \n63      Lammps        0.1     None       NaN  \n\n[64 rows x 15 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>status</th>\n      <th>chemicalformula</th>\n      <th>job</th>\n      <th>subjob</th>\n      <th>projectpath</th>\n      <th>project</th>\n      <th>timestart</th>\n      <th>timestop</th>\n      <th>totalcputime</th>\n      <th>computer</th>\n      <th>hamilton</th>\n      <th>hamversion</th>\n      <th>parentid</th>\n      <th>masterid</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>finished</td>\n      <td>Fe2</td>\n      <td>job_mini</td>\n      <td>/job_mini</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:51:52.778780</td>\n      <td>2021-03-22 23:51:53.409059</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1</td>\n      <td>Lammps</td>\n      <td>0.1</td>\n      <td>None</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_0_9</td>\n      <td>/lmp_0_9</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:51:54.545651</td>\n      <td>2021-03-22 23:51:55.084443</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1</td>\n      <td>Lammps</td>\n      <td>0.1</td>\n      <td>None</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_0_92</td>\n      <td>/lmp_0_92</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:51:55.922362</td>\n      <td>2021-03-22 23:51:56.454064</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1</td>\n      <td>Lammps</td>\n      <td>0.1</td>\n      <td>None</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_0_94</td>\n      <td>/lmp_0_94</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:51:57.354493</td>\n      <td>2021-03-22 23:51:57.921399</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1</td>\n      <td>Lammps</td>\n      <td>0.1</td>\n      <td>None</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp_0_96</td>\n      <td>/lmp_0_96</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:51:58.821766</td>\n      <td>2021-03-22 23:51:59.324438</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1</td>\n      <td>Lammps</td>\n      <td>0.1</td>\n      <td>None</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>61</td>\n      <td>finished</td>\n      <td>Fe128</td>\n      <td>job_strain_0</td>\n      <td>/job_strain_0</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/quasi_strain_hdf5/strain_1_08_hdf5/</td>\n      <td>2021-03-22 23:54:24.027821</td>\n      <td>2021-03-22 23:54:24.729845</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1</td>\n      <td>Lammps</td>\n      <td>0.1</td>\n      <td>None</td>\n      <td>60.0</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>62</td>\n      <td>finished</td>\n      <td>Fe2</td>\n      <td>strain_1_1</td>\n      <td>/strain_1_1</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/quasi_strain_hdf5/</td>\n      <td>2021-03-22 23:54:28.338953</td>\n      <td>2021-03-22 23:54:34.997655</td>\n      <td>6.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1#1/1</td>\n      <td>PhonopyJob</td>\n      <td>0.0.1</td>\n      <td>None</td>\n      <td>41.0</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>63</td>\n      <td>finished</td>\n      <td>Fe128</td>\n      <td>job_strain_0</td>\n      <td>/job_strain_0</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/quasi_strain_hdf5/strain_1_1_hdf5/</td>\n      <td>2021-03-22 23:54:31.821842</td>\n      <td>2021-03-22 23:54:32.536800</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1</td>\n      <td>Lammps</td>\n      <td>0.1</td>\n      <td>None</td>\n      <td>62.0</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>64</td>\n      <td>finished</td>\n      <td>None</td>\n      <td>script</td>\n      <td>/script</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/</td>\n      <td>2021-03-22 23:54:50.762741</td>\n      <td>2021-03-22 23:54:55.087347</td>\n      <td>4.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1</td>\n      <td>Script</td>\n      <td>0.1</td>\n      <td>None</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>65</td>\n      <td>finished</td>\n      <td>Fe</td>\n      <td>lmp</td>\n      <td>/lmp</td>\n      <td>/home/jovyan/</td>\n      <td>scaleup/script_hdf5/script/script/</td>\n      <td>2021-03-22 23:54:53.820840</td>\n      <td>2021-03-22 23:54:54.309320</td>\n      <td>0.0</td>\n      <td>pyiron@jupyter-pyiron-2dswimm-2dworkshop-2d2021-2dw6x4leku#1</td>\n      <td>Lammps</td>\n      <td>0.1</td>\n      <td>None</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>64 rows × 15 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"id":"above-thanksgiving","cell_type":"markdown","source":"Therefore while offering less flexibility the `ScriptJob` class is a simple way to create reusable snippets if certain parts of a simulation protocol are commonly repeated in different projects. "},{"metadata":{},"id":"reverse-promotion","cell_type":"markdown","source":"## Runtime communication\nThe third way to accelerate existing simulation protocols in pyiron is by using the so-called interactive mode. This interactive mode is enabled by simply setting the flag `job.server.run_mode.interactive = True` and it enables communication with serial and parallel simulation codes during run time by leveraging various communication interfaces. For example: \n* LAMMPS is available as Python library so in process communication is easy. \n* VASP provides interactive communication via standard input and output in combination with the `INTERACTIVE` tag set in the `INCAR` file which is unfortunatley not officially documented. \n* S/PHI/nX uses unix pipes to communicate with python during run time. \n\npyiron implements a generic interface for these different communication standards, compareable to [ipi-code](http://ipi-code.org) and again enables the user to interact with the codes interactively by keeping the simulation code running in the background. In combination with the `Murnaghan` job a single `LAMMPS` calculation can be used as quantum engine to evaluate 11 different strains. "},{"metadata":{"trusted":true},"id":"following-employment","cell_type":"code","source":"job_murn = job_fe.copy_template(\n    new_job_name=\"job_murn_int\"\n)\njob_murn.server.run_mode.interactive = True\nmurn = job_murn.create_job(\n    job_type=pr.job_type.Murnaghan, \n    job_name=\"murn_int\"\n)\nmurn.run()\nmurn.plot()","execution_count":36,"outputs":[{"output_type":"stream","text":"The job murn_int was saved and received the ID: 66\nThe job murn_int_job_murn_int was saved and received the ID: 67\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"id":"innocent-jewelry","cell_type":"markdown","source":"## Summary \nAfter the general introduction to pyiron on the first day the second session introduced multiple methods to up-scale existing simulation protocols: \n* Starting with `GenericMaster` classes which can be combined like building blocks. \n* Followed by the `ScriptJob` class which enables the easy reuse of existing simulation protocols. \n* Finally the interactive mode was briefly introduced as a way to accelerate calculation by keeping the simulation code running during the execution. \n\nAdditional exercises: \n* Extend the evaluation of interatomic potentials from the first day by comparing the density of states for different interatomic potentials. How does the sensitivity of bulk properties like the bulk modulus compare to the sensitivity of the free energy of different interatomic potentials? \n* Write a `ScriptJob` for a Murnaghan calculation, by using the `script_job.input` to set the input for the `ScriptJob` and then inside the `ScriptJob` use `pr.get_external_input()` to retrieve the input. \n* Implement a simple MonteCarlo algorithm for LAMMPS by changing the atom species of the `job.structure` object and calling `job.run()` after each change to evaluate the structure in interactive mode. By setting `job.interactive_enforce_structure_reset = True` the structure is always reset for each step, while the flag is typically set to false for applications like calculating molecular dynamics trajectories or thermodynamic integration. "},{"metadata":{"trusted":true},"id":"special-trailer","cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":5}