{
"cells": [
{
"cell_type": "markdown",
"id": "25d5b0d5-f330-4dcb-9b7c-f57c4bea9596",
"metadata": {},
"source": [
"# **Workshop: From electrons to phase diagrams**\n",
"\n",
"# Day 2: Validation of the potentials (draft)"
]
},
{
"cell_type": "markdown",
"id": "4756d4c9-304a-4ccc-b772-ba67d008c5a4",
"metadata": {},
"source": [
"## Import the fitted potentials for Li-Al ( from prevoius excercise)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "83f7a2c9-d45a-4987-9e35-59badd754d4f",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pylab as plt\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"import time\n",
"from helper import potentials_list, get_clean_project_name"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "73bedeef-3e9b-4ca4-99c1-1be729222df8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.5625"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"time_start = time.process_time()\n",
"time_start"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "da96c965-24f8-401d-911e-72818c90e731",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pyiron_atomistics import Project\n",
"import pyiron_gpl\n",
"# from ase.lattice.compounds import B2\n",
"# from pyiron_atomistics import ase_to_pyiron"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "abf1afd7-a6a1-46b5-99f1-6d74f5986cb1",
"metadata": {},
"outputs": [],
"source": [
"# from structdbrest import StructDBLightRester\n",
"# rest = StructDBLightRester(token=\"workshop2021\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "706be2a9-5f94-4eb5-8e4f-6c349fe216b3",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d407e4af1e84824992ef6a32b9779dd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/148 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pr = Project(\"validation_LiAl\")\n",
"pr.remove_jobs(silently=True, recursive=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9bd63883-25b3-441f-9b4b-b3c960a179aa",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Filename</th>\n",
" <th>Model</th>\n",
" <th>Species</th>\n",
" <th>Config</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LiAl_eam</td>\n",
" <td>[/home/surendralal/notebooks/workshop_preparation/potentials/AlLi.eam.fs]</td>\n",
" <td>EAM</td>\n",
" <td>[Li, Al]</td>\n",
" <td>[pair_style eam/fs\\n, pair_coeff * * AlLi.eam.fs Li Al\\n]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name \\\n",
"0 LiAl_eam \n",
"\n",
" Filename \\\n",
"0 [/home/surendralal/notebooks/workshop_preparation/potentials/AlLi.eam.fs] \n",
"\n",
" Model Species Config \n",
"0 EAM [Li, Al] [pair_style eam/fs\\n, pair_coeff * * AlLi.eam.fs Li Al\\n] "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"potentials_list[0]"
]
},
{
"cell_type": "markdown",
"id": "3b84ed62-e841-4526-893e-dc4f61477c88",
"metadata": {},
"source": [
"## Iterate over all potentials and all possible phases"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "28778cef-2a07-4794-888f-7239500e7b5a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Al': {'s_murn': ['fcc', 'bcc'], 'a': 4.04},\n",
" 'Li': {'s_murn': ['fcc', 'bcc'], 'a': 3.5},\n",
" 'LiAl': {'s_murn': ['B2'], 'a': 3.7}}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"struct_dict = dict()\n",
"struct_dict[\"Al\"] = dict()\n",
"struct_dict[\"Al\"][\"s_murn\"] = [\"fcc\", \"bcc\"]\n",
"struct_dict[\"Al\"][\"a\"] = 4.04\n",
"\n",
"struct_dict[\"Li\"] = dict()\n",
"struct_dict[\"Li\"][\"s_murn\"] = [\"fcc\", \"bcc\"]\n",
"struct_dict[\"Li\"][\"a\"] = 3.5\n",
"\n",
"\n",
"struct_dict[\"LiAl\"] = dict()\n",
"struct_dict[\"LiAl\"][\"s_murn\"] = [\"B2\"]\n",
"struct_dict[\"LiAl\"][\"a\"] = 3.7\n",
"\n",
"struct_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdc720f8-0a31-4be4-a47a-9909f23e4442",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "198e9745-734a-4502-8f1b-0330ba8c8fca",
"metadata": {},
"source": [
"### Ground state: E-V curves"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "13f095d2-44d7-4711-b9a5-d58a95af42f6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job Al_fcc_relax was saved and received the ID: 616\n",
"The job murn_job_Al_fcc was saved and received the ID: 617\n",
"The job murn_job_Al_fcc_0_9 was saved and received the ID: 618\n",
"The job murn_job_Al_fcc_0_92 was saved and received the ID: 619\n",
"The job murn_job_Al_fcc_0_94 was saved and received the ID: 620\n",
"The job murn_job_Al_fcc_0_96 was saved and received the ID: 621\n",
"The job murn_job_Al_fcc_0_98 was saved and received the ID: 622\n",
"The job murn_job_Al_fcc_1_0 was saved and received the ID: 623\n",
"The job murn_job_Al_fcc_1_02 was saved and received the ID: 624\n",
"The job murn_job_Al_fcc_1_04 was saved and received the ID: 625\n",
"The job murn_job_Al_fcc_1_06 was saved and received the ID: 626\n",
"The job murn_job_Al_fcc_1_08 was saved and received the ID: 627\n",
"The job murn_job_Al_fcc_1_1 was saved and received the ID: 628\n",
"The job Al_bcc_relax was saved and received the ID: 629\n",
"The job murn_job_Al_bcc was saved and received the ID: 630\n",
"The job murn_job_Al_bcc_0_9 was saved and received the ID: 631\n",
"The job murn_job_Al_bcc_0_92 was saved and received the ID: 632\n",
"The job murn_job_Al_bcc_0_94 was saved and received the ID: 633\n",
"The job murn_job_Al_bcc_0_96 was saved and received the ID: 634\n",
"The job murn_job_Al_bcc_0_98 was saved and received the ID: 635\n",
"The job murn_job_Al_bcc_1_0 was saved and received the ID: 636\n",
"The job murn_job_Al_bcc_1_02 was saved and received the ID: 637\n",
"The job murn_job_Al_bcc_1_04 was saved and received the ID: 638\n",
"The job murn_job_Al_bcc_1_06 was saved and received the ID: 639\n",
"The job murn_job_Al_bcc_1_08 was saved and received the ID: 640\n",
"The job murn_job_Al_bcc_1_1 was saved and received the ID: 641\n",
"The job Li_fcc_relax was saved and received the ID: 642\n",
"The job murn_job_Li_fcc was saved and received the ID: 643\n",
"The job murn_job_Li_fcc_0_9 was saved and received the ID: 644\n",
"The job murn_job_Li_fcc_0_92 was saved and received the ID: 645\n",
"The job murn_job_Li_fcc_0_94 was saved and received the ID: 646\n",
"The job murn_job_Li_fcc_0_96 was saved and received the ID: 647\n",
"The job murn_job_Li_fcc_0_98 was saved and received the ID: 648\n",
"The job murn_job_Li_fcc_1_0 was saved and received the ID: 649\n",
"The job murn_job_Li_fcc_1_02 was saved and received the ID: 650\n",
"The job murn_job_Li_fcc_1_04 was saved and received the ID: 651\n",
"The job murn_job_Li_fcc_1_06 was saved and received the ID: 652\n",
"The job murn_job_Li_fcc_1_08 was saved and received the ID: 653\n",
"The job murn_job_Li_fcc_1_1 was saved and received the ID: 654\n",
"The job Li_bcc_relax was saved and received the ID: 655\n",
"The job murn_job_Li_bcc was saved and received the ID: 656\n",
"The job murn_job_Li_bcc_0_9 was saved and received the ID: 657\n",
"The job murn_job_Li_bcc_0_92 was saved and received the ID: 658\n",
"The job murn_job_Li_bcc_0_94 was saved and received the ID: 659\n",
"The job murn_job_Li_bcc_0_96 was saved and received the ID: 660\n",
"The job murn_job_Li_bcc_0_98 was saved and received the ID: 661\n",
"The job murn_job_Li_bcc_1_0 was saved and received the ID: 662\n",
"The job murn_job_Li_bcc_1_02 was saved and received the ID: 663\n",
"The job murn_job_Li_bcc_1_04 was saved and received the ID: 664\n",
"The job murn_job_Li_bcc_1_06 was saved and received the ID: 665\n",
"The job murn_job_Li_bcc_1_08 was saved and received the ID: 666\n",
"The job murn_job_Li_bcc_1_1 was saved and received the ID: 667\n",
"The job LiAl_B2_relax was saved and received the ID: 668\n",
"The job murn_job_LiAl_B2 was saved and received the ID: 669\n",
"The job murn_job_LiAl_B2_0_9 was saved and received the ID: 670\n",
"The job murn_job_LiAl_B2_0_92 was saved and received the ID: 671\n",
"The job murn_job_LiAl_B2_0_94 was saved and received the ID: 672\n",
"The job murn_job_LiAl_B2_0_96 was saved and received the ID: 673\n",
"The job murn_job_LiAl_B2_0_98 was saved and received the ID: 674\n",
"The job murn_job_LiAl_B2_1_0 was saved and received the ID: 675\n",
"The job murn_job_LiAl_B2_1_02 was saved and received the ID: 676\n",
"The job murn_job_LiAl_B2_1_04 was saved and received the ID: 677\n",
"The job murn_job_LiAl_B2_1_06 was saved and received the ID: 678\n",
"The job murn_job_LiAl_B2_1_08 was saved and received the ID: 679\n",
"The job murn_job_LiAl_B2_1_1 was saved and received the ID: 680\n"
]
}
],
"source": [
"for pot in potentials_list:\n",
" with pr.open(get_clean_project_name(pot)) as pr_pot:\n",
" for compound, compound_dict in struct_dict.items():\n",
" for crys_structure in compound_dict[\"s_murn\"]:\n",
" \n",
" # Relax structure\n",
" if crys_structure == \"B2\":\n",
" basis = pr.create.structure.compound.B2(\"Li\", \"Al\", a=compound_dict[\"a\"])\n",
" else:\n",
" basis = pr_pot.create_ase_bulk(compound, crys_structure, a=compound_dict[\"a\"])\n",
" job_relax = pr_pot.create_job(pr_pot.job_type.Lammps, f\"{compound}_{crys_structure}_relax\", delete_existing_job=True)\n",
"\n",
" job_relax.structure = basis\n",
" job_relax.potential = pot\n",
" job_relax.calc_minimize(pressure=0)\n",
" job_relax.run()\n",
" \n",
" # Murnaghan\n",
" job_ref = pr_pot.create_job(pr_pot.job_type.Lammps, f\"ref_job_{compound}_{crys_structure}\")\n",
" job_ref.structure = job_relax.get_structure(-1)\n",
" job_ref.potential = pot\n",
" job_ref.calc_minimize()\n",
" murn_job = job_ref.create_job(pr_pot.job_type.Murnaghan, f\"murn_job_{compound}_{crys_structure}\")\n",
" murn_job.input[\"vol_range\"] = 0.1\n",
" murn_job.run()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "fdc89ebb-3c2a-4315-8fe0-3ae470375223",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>status</th>\n",
" <th>chemicalformula</th>\n",
" <th>job</th>\n",
" <th>subjob</th>\n",
" <th>projectpath</th>\n",
" <th>project</th>\n",
" <th>timestart</th>\n",
" <th>timestop</th>\n",
" <th>totalcputime</th>\n",
" <th>computer</th>\n",
" <th>hamilton</th>\n",
" <th>hamversion</th>\n",
" <th>parentid</th>\n",
" <th>masterid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>616</td>\n",
" <td>finished</td>\n",
" <td>Al</td>\n",
" <td>Al_fcc_relax</td>\n",
" <td>/Al_fcc_relax</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/</td>\n",
" <td>2022-05-04 22:49:05.133150</td>\n",
" <td>2022-05-04 22:49:05.498424</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>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>617</td>\n",
" <td>finished</td>\n",
" <td>Al</td>\n",
" <td>murn_job_Al_fcc</td>\n",
" <td>/murn_job_Al_fcc</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/</td>\n",
" <td>2022-05-04 22:49:06.215829</td>\n",
" <td>2022-05-04 22:49:22.913078</td>\n",
" <td>16.0</td>\n",
" <td>pyiron@cmdell17#1#11/11</td>\n",
" <td>Murnaghan</td>\n",
" <td>0.3.0</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>618</td>\n",
" <td>finished</td>\n",
" <td>Al</td>\n",
" <td>murn_job_Al_fcc_0_9</td>\n",
" <td>/murn_job_Al_fcc_0_9</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_Al_fcc_hdf5/</td>\n",
" <td>2022-05-04 22:49:06.940592</td>\n",
" <td>2022-05-04 22:49:07.319269</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>617.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>619</td>\n",
" <td>finished</td>\n",
" <td>Al</td>\n",
" <td>murn_job_Al_fcc_0_92</td>\n",
" <td>/murn_job_Al_fcc_0_92</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_Al_fcc_hdf5/</td>\n",
" <td>2022-05-04 22:49:08.269957</td>\n",
" <td>2022-05-04 22:49:08.637426</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>617.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>620</td>\n",
" <td>finished</td>\n",
" <td>Al</td>\n",
" <td>murn_job_Al_fcc_0_94</td>\n",
" <td>/murn_job_Al_fcc_0_94</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_Al_fcc_hdf5/</td>\n",
" <td>2022-05-04 22:49:09.584156</td>\n",
" <td>2022-05-04 22:49:09.974398</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>617.0</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>60</th>\n",
" <td>676</td>\n",
" <td>finished</td>\n",
" <td>AlLi</td>\n",
" <td>murn_job_LiAl_B2_1_02</td>\n",
" <td>/murn_job_LiAl_B2_1_02</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_LiAl_B2_hdf5/</td>\n",
" <td>2022-05-04 22:50:35.979262</td>\n",
" <td>2022-05-04 22:50:36.372339</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>669.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>677</td>\n",
" <td>finished</td>\n",
" <td>AlLi</td>\n",
" <td>murn_job_LiAl_B2_1_04</td>\n",
" <td>/murn_job_LiAl_B2_1_04</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_LiAl_B2_hdf5/</td>\n",
" <td>2022-05-04 22:50:37.429723</td>\n",
" <td>2022-05-04 22:50:37.807216</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>669.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>678</td>\n",
" <td>finished</td>\n",
" <td>AlLi</td>\n",
" <td>murn_job_LiAl_B2_1_06</td>\n",
" <td>/murn_job_LiAl_B2_1_06</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_LiAl_B2_hdf5/</td>\n",
" <td>2022-05-04 22:50:38.858581</td>\n",
" <td>2022-05-04 22:50:39.272040</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>669.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>679</td>\n",
" <td>finished</td>\n",
" <td>AlLi</td>\n",
" <td>murn_job_LiAl_B2_1_08</td>\n",
" <td>/murn_job_LiAl_B2_1_08</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_LiAl_B2_hdf5/</td>\n",
" <td>2022-05-04 22:50:40.299460</td>\n",
" <td>2022-05-04 22:50:40.688724</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>669.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>680</td>\n",
" <td>finished</td>\n",
" <td>AlLi</td>\n",
" <td>murn_job_LiAl_B2_1_1</td>\n",
" <td>/murn_job_LiAl_B2_1_1</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_LiAl_B2_hdf5/</td>\n",
" <td>2022-05-04 22:50:41.759774</td>\n",
" <td>2022-05-04 22:50:42.169540</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>669.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>65 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" id status chemicalformula job \\\n",
"0 616 finished Al Al_fcc_relax \n",
"1 617 finished Al murn_job_Al_fcc \n",
"2 618 finished Al murn_job_Al_fcc_0_9 \n",
"3 619 finished Al murn_job_Al_fcc_0_92 \n",
"4 620 finished Al murn_job_Al_fcc_0_94 \n",
".. ... ... ... ... \n",
"60 676 finished AlLi murn_job_LiAl_B2_1_02 \n",
"61 677 finished AlLi murn_job_LiAl_B2_1_04 \n",
"62 678 finished AlLi murn_job_LiAl_B2_1_06 \n",
"63 679 finished AlLi murn_job_LiAl_B2_1_08 \n",
"64 680 finished AlLi murn_job_LiAl_B2_1_1 \n",
"\n",
" subjob projectpath \\\n",
"0 /Al_fcc_relax None \n",
"1 /murn_job_Al_fcc None \n",
"2 /murn_job_Al_fcc_0_9 None \n",
"3 /murn_job_Al_fcc_0_92 None \n",
"4 /murn_job_Al_fcc_0_94 None \n",
".. ... ... \n",
"60 /murn_job_LiAl_B2_1_02 None \n",
"61 /murn_job_LiAl_B2_1_04 None \n",
"62 /murn_job_LiAl_B2_1_06 None \n",
"63 /murn_job_LiAl_B2_1_08 None \n",
"64 /murn_job_LiAl_B2_1_1 None \n",
"\n",
" project \\\n",
"0 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/ \n",
"1 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/ \n",
"2 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_Al_fcc_hdf5/ \n",
"3 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_Al_fcc_hdf5/ \n",
"4 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_Al_fcc_hdf5/ \n",
".. ... \n",
"60 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_LiAl_B2_hdf5/ \n",
"61 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_LiAl_B2_hdf5/ \n",
"62 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_LiAl_B2_hdf5/ \n",
"63 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_LiAl_B2_hdf5/ \n",
"64 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/murn_job_LiAl_B2_hdf5/ \n",
"\n",
" timestart timestop totalcputime \\\n",
"0 2022-05-04 22:49:05.133150 2022-05-04 22:49:05.498424 0.0 \n",
"1 2022-05-04 22:49:06.215829 2022-05-04 22:49:22.913078 16.0 \n",
"2 2022-05-04 22:49:06.940592 2022-05-04 22:49:07.319269 0.0 \n",
"3 2022-05-04 22:49:08.269957 2022-05-04 22:49:08.637426 0.0 \n",
"4 2022-05-04 22:49:09.584156 2022-05-04 22:49:09.974398 0.0 \n",
".. ... ... ... \n",
"60 2022-05-04 22:50:35.979262 2022-05-04 22:50:36.372339 0.0 \n",
"61 2022-05-04 22:50:37.429723 2022-05-04 22:50:37.807216 0.0 \n",
"62 2022-05-04 22:50:38.858581 2022-05-04 22:50:39.272040 0.0 \n",
"63 2022-05-04 22:50:40.299460 2022-05-04 22:50:40.688724 0.0 \n",
"64 2022-05-04 22:50:41.759774 2022-05-04 22:50:42.169540 0.0 \n",
"\n",
" computer hamilton hamversion parentid masterid \n",
"0 pyiron@cmdell17#1 Lammps 0.1 None NaN \n",
"1 pyiron@cmdell17#1#11/11 Murnaghan 0.3.0 None NaN \n",
"2 pyiron@cmdell17#1 Lammps 0.1 None 617.0 \n",
"3 pyiron@cmdell17#1 Lammps 0.1 None 617.0 \n",
"4 pyiron@cmdell17#1 Lammps 0.1 None 617.0 \n",
".. ... ... ... ... ... \n",
"60 pyiron@cmdell17#1 Lammps 0.1 None 669.0 \n",
"61 pyiron@cmdell17#1 Lammps 0.1 None 669.0 \n",
"62 pyiron@cmdell17#1 Lammps 0.1 None 669.0 \n",
"63 pyiron@cmdell17#1 Lammps 0.1 None 669.0 \n",
"64 pyiron@cmdell17#1 Lammps 0.1 None 669.0 \n",
"\n",
"[65 rows x 15 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pr.job_table()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ef2f414b-64b8-49aa-87e9-e204950da938",
"metadata": {},
"outputs": [],
"source": [
"# Define functions to get data\n",
"\n",
"# Only work with Murnaghan jobs\n",
"def get_only_murn(job_table):\n",
" return (job_table.hamilton == \"Murnaghan\") & (job_table.status == \"finished\") \n",
"\n",
"def get_eq_vol(job_path):\n",
" return job_path[\"output/equilibrium_volume\"]\n",
"\n",
"def get_eq_lp(job_path):\n",
" 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\"]\n",
"\n",
"def get_potential(job_path):\n",
" return job_path.project.path.split(\"/\")[-3]\n",
"\n",
"def get_eq_energy(job_path):\n",
" return job_path[\"output/equilibrium_energy\"]\n",
"\n",
"def get_n_atoms(job_path):\n",
" return len(job_path[\"output/structure/positions\"])\n",
"\n",
"\n",
"def get_potential(job_path):\n",
" return job_path.project.path.split(\"/\")[-2]\n",
"\n",
"def get_crystal_structure(job_path):\n",
" return job_path.job_name.split(\"_\")[-1]\n",
"\n",
"def get_compound(job_path):\n",
" return job_path.job_name.split(\"_\")[-2]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "255c28af-e4af-48c6-ae01-e90377c94e32",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job table_murn was saved and received the ID: 681\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a2c081228ccb4979a1b3c0e03513b91b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading and filtering jobs: 0%| | 0/5 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bcdbdbd52be7440cb5f118fa42f769e5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Processing jobs: 0%| | 0/5 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compile data using pyiron tables\n",
"table = pr.create_table(\"table_murn\", delete_existing_job=True)\n",
"table.convert_to_object = True\n",
"table.db_filter_function = get_only_murn\n",
"table.add[\"potential\"] = get_potential\n",
"table.add[\"compound\"] = get_compound\n",
"table.add[\"crystal_structure\"] = get_crystal_structure\n",
"table.add[\"a\"] = get_eq_lp\n",
"table.add[\"eq_vol\"] = get_eq_vol\n",
"table.add[\"eq_bm\"] = get_eq_bm\n",
"table.add[\"eq_energy\"] = get_eq_energy\n",
"table.add[\"n_atoms\"] = get_n_atoms\n",
"table.run()\n",
"data_murn = table.get_dataframe()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "240863b0-1934-4f83-abc1-69ad1796cca8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>status</th>\n",
" <th>chemicalformula</th>\n",
" <th>job</th>\n",
" <th>subjob</th>\n",
" <th>projectpath</th>\n",
" <th>project</th>\n",
" <th>timestart</th>\n",
" <th>timestop</th>\n",
" <th>totalcputime</th>\n",
" <th>computer</th>\n",
" <th>hamilton</th>\n",
" <th>hamversion</th>\n",
" <th>parentid</th>\n",
" <th>masterid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>617</td>\n",
" <td>finished</td>\n",
" <td>Al</td>\n",
" <td>murn_job_Al_fcc</td>\n",
" <td>/murn_job_Al_fcc</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/</td>\n",
" <td>2022-05-04 22:49:06.215829</td>\n",
" <td>2022-05-04 22:49:22.913078</td>\n",
" <td>16.0</td>\n",
" <td>pyiron@cmdell17#1#11/11</td>\n",
" <td>Murnaghan</td>\n",
" <td>0.3.0</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>630</td>\n",
" <td>finished</td>\n",
" <td>Al</td>\n",
" <td>murn_job_Al_bcc</td>\n",
" <td>/murn_job_Al_bcc</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/</td>\n",
" <td>2022-05-04 22:49:24.807002</td>\n",
" <td>2022-05-04 22:49:41.327694</td>\n",
" <td>16.0</td>\n",
" <td>pyiron@cmdell17#1#11/11</td>\n",
" <td>Murnaghan</td>\n",
" <td>0.3.0</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>643</td>\n",
" <td>finished</td>\n",
" <td>Li</td>\n",
" <td>murn_job_Li_fcc</td>\n",
" <td>/murn_job_Li_fcc</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/</td>\n",
" <td>2022-05-04 22:49:43.066799</td>\n",
" <td>2022-05-04 22:50:04.332253</td>\n",
" <td>21.0</td>\n",
" <td>pyiron@cmdell17#1#11/11</td>\n",
" <td>Murnaghan</td>\n",
" <td>0.3.0</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>656</td>\n",
" <td>finished</td>\n",
" <td>Li</td>\n",
" <td>murn_job_Li_bcc</td>\n",
" <td>/murn_job_Li_bcc</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/</td>\n",
" <td>2022-05-04 22:50:06.470703</td>\n",
" <td>2022-05-04 22:50:24.470539</td>\n",
" <td>17.0</td>\n",
" <td>pyiron@cmdell17#1#11/11</td>\n",
" <td>Murnaghan</td>\n",
" <td>0.3.0</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>669</td>\n",
" <td>finished</td>\n",
" <td>AlLi</td>\n",
" <td>murn_job_LiAl_B2</td>\n",
" <td>/murn_job_LiAl_B2</td>\n",
" <td>None</td>\n",
" <td>/home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/</td>\n",
" <td>2022-05-04 22:50:26.442419</td>\n",
" <td>2022-05-04 22:50:42.830226</td>\n",
" <td>16.0</td>\n",
" <td>pyiron@cmdell17#1#11/11</td>\n",
" <td>Murnaghan</td>\n",
" <td>0.3.0</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id status chemicalformula job subjob \\\n",
"1 617 finished Al murn_job_Al_fcc /murn_job_Al_fcc \n",
"14 630 finished Al murn_job_Al_bcc /murn_job_Al_bcc \n",
"27 643 finished Li murn_job_Li_fcc /murn_job_Li_fcc \n",
"40 656 finished Li murn_job_Li_bcc /murn_job_Li_bcc \n",
"53 669 finished AlLi murn_job_LiAl_B2 /murn_job_LiAl_B2 \n",
"\n",
" projectpath \\\n",
"1 None \n",
"14 None \n",
"27 None \n",
"40 None \n",
"53 None \n",
"\n",
" project \\\n",
"1 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/ \n",
"14 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/ \n",
"27 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/ \n",
"40 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/ \n",
"53 /home/surendralal/notebooks/workshop_preparation/validation/validation_LiAl/LiAl_eam/ \n",
"\n",
" timestart timestop totalcputime \\\n",
"1 2022-05-04 22:49:06.215829 2022-05-04 22:49:22.913078 16.0 \n",
"14 2022-05-04 22:49:24.807002 2022-05-04 22:49:41.327694 16.0 \n",
"27 2022-05-04 22:49:43.066799 2022-05-04 22:50:04.332253 21.0 \n",
"40 2022-05-04 22:50:06.470703 2022-05-04 22:50:24.470539 17.0 \n",
"53 2022-05-04 22:50:26.442419 2022-05-04 22:50:42.830226 16.0 \n",
"\n",
" computer hamilton hamversion parentid masterid \n",
"1 pyiron@cmdell17#1#11/11 Murnaghan 0.3.0 None NaN \n",
"14 pyiron@cmdell17#1#11/11 Murnaghan 0.3.0 None NaN \n",
"27 pyiron@cmdell17#1#11/11 Murnaghan 0.3.0 None NaN \n",
"40 pyiron@cmdell17#1#11/11 Murnaghan 0.3.0 None NaN \n",
"53 pyiron@cmdell17#1#11/11 Murnaghan 0.3.0 None NaN "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pr.job_table(status=\"finished\", hamilton=\"Murnaghan\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "59aa0440-bca6-4625-8a63-c875a67e9248",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
" <th>potential</th>\n",
" <th>compound</th>\n",
" <th>crystal_structure</th>\n",
" <th>a</th>\n",
" <th>eq_vol</th>\n",
" <th>eq_bm</th>\n",
" <th>eq_energy</th>\n",
" <th>n_atoms</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>617</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.039967</td>\n",
" <td>16.495612</td>\n",
" <td>85.876912</td>\n",
" <td>-3.483097</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>630</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.898853</td>\n",
" <td>16.147864</td>\n",
" <td>48.620841</td>\n",
" <td>-3.415312</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>643</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>4.253841</td>\n",
" <td>19.241330</td>\n",
" <td>13.985972</td>\n",
" <td>-1.758107</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>656</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.195477</td>\n",
" <td>20.114514</td>\n",
" <td>13.690609</td>\n",
" <td>-1.757011</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>669</td>\n",
" <td>LiAl_eam</td>\n",
" <td>LiAl</td>\n",
" <td>B2</td>\n",
" <td>4.341228</td>\n",
" <td>28.939499</td>\n",
" <td>71.555056</td>\n",
" <td>-5.300981</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential compound crystal_structure a eq_vol \\\n",
"0 617 LiAl_eam Al fcc 4.039967 16.495612 \n",
"1 630 LiAl_eam Al bcc 3.898853 16.147864 \n",
"2 643 LiAl_eam Li fcc 4.253841 19.241330 \n",
"3 656 LiAl_eam Li bcc 4.195477 20.114514 \n",
"4 669 LiAl_eam LiAl B2 4.341228 28.939499 \n",
"\n",
" eq_bm eq_energy n_atoms \n",
"0 85.876912 -3.483097 1 \n",
"1 48.620841 -3.415312 1 \n",
"2 13.985972 -1.758107 1 \n",
"3 13.690609 -1.757011 1 \n",
"4 71.555056 -5.300981 2 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_murn"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "7cc5f491-a4f5-450e-919a-d387d0423fd7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.966650237012193"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"4.195477 / np.sqrt(2)"
]
},
{
"cell_type": "markdown",
"id": "fba90359-a2a5-4f83-9fa8-6dc4d87f5743",
"metadata": {},
"source": [
"## Elastic constants and Phonons"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "0d1c799c-f10b-462d-aaea-253cee4b4b3e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LiAl_eam\n",
"The job elastic_job_Al_fcc was saved and received the ID: 682\n",
"The job s_e_0 was saved and received the ID: 683\n",
"The job s_01_e_m0_05000 was saved and received the ID: 684\n",
"The job s_01_e_m0_02500 was saved and received the ID: 685\n",
"The job s_01_e_0_02500 was saved and received the ID: 686\n",
"The job s_01_e_0_05000 was saved and received the ID: 687\n",
"The job s_08_e_m0_05000 was saved and received the ID: 688\n",
"The job s_08_e_m0_02500 was saved and received the ID: 689\n",
"The job s_08_e_0_02500 was saved and received the ID: 690\n",
"The job s_08_e_0_05000 was saved and received the ID: 691\n",
"The job s_23_e_m0_05000 was saved and received the ID: 692\n",
"The job s_23_e_m0_02500 was saved and received the ID: 693\n",
"The job s_23_e_0_02500 was saved and received the ID: 694\n",
"The job s_23_e_0_05000 was saved and received the ID: 695\n",
"The job phonopy_job_Al_fcc was saved and received the ID: 696\n",
"The job ref_job_Al_fcc_0 was saved and received the ID: 697\n",
"The job elastic_job_Al_bcc was saved and received the ID: 698\n",
"The job s_e_0 was saved and received the ID: 699\n",
"The job s_01_e_m0_05000 was saved and received the ID: 700\n",
"The job s_01_e_m0_02500 was saved and received the ID: 701\n",
"The job s_01_e_0_02500 was saved and received the ID: 702\n",
"The job s_01_e_0_05000 was saved and received the ID: 703\n",
"The job s_08_e_m0_05000 was saved and received the ID: 704\n",
"The job s_08_e_m0_02500 was saved and received the ID: 705\n",
"The job s_08_e_0_02500 was saved and received the ID: 706\n",
"The job s_08_e_0_05000 was saved and received the ID: 707\n",
"The job s_23_e_m0_05000 was saved and received the ID: 708\n",
"The job s_23_e_m0_02500 was saved and received the ID: 709\n",
"The job s_23_e_0_02500 was saved and received the ID: 710\n",
"The job s_23_e_0_05000 was saved and received the ID: 711\n",
"The job phonopy_job_Al_bcc was saved and received the ID: 712\n",
"The job ref_job_Al_bcc_0 was saved and received the ID: 713\n",
"The job elastic_job_Li_fcc was saved and received the ID: 714\n",
"The job s_e_0 was saved and received the ID: 715\n",
"The job s_01_e_m0_05000 was saved and received the ID: 716\n",
"The job s_01_e_m0_02500 was saved and received the ID: 717\n",
"The job s_01_e_0_02500 was saved and received the ID: 718\n",
"The job s_01_e_0_05000 was saved and received the ID: 719\n",
"The job s_08_e_m0_05000 was saved and received the ID: 720\n",
"The job s_08_e_m0_02500 was saved and received the ID: 721\n",
"The job s_08_e_0_02500 was saved and received the ID: 722\n",
"The job s_08_e_0_05000 was saved and received the ID: 723\n",
"The job s_23_e_m0_05000 was saved and received the ID: 724\n",
"The job s_23_e_m0_02500 was saved and received the ID: 725\n",
"The job s_23_e_0_02500 was saved and received the ID: 726\n",
"The job s_23_e_0_05000 was saved and received the ID: 727\n",
"The job phonopy_job_Li_fcc was saved and received the ID: 728\n",
"The job ref_job_Li_fcc_0 was saved and received the ID: 729\n",
"The job elastic_job_Li_bcc was saved and received the ID: 730\n",
"The job s_e_0 was saved and received the ID: 731\n",
"The job s_01_e_m0_05000 was saved and received the ID: 732\n",
"The job s_01_e_m0_02500 was saved and received the ID: 733\n",
"The job s_01_e_0_02500 was saved and received the ID: 734\n",
"The job s_01_e_0_05000 was saved and received the ID: 735\n",
"The job s_08_e_m0_05000 was saved and received the ID: 736\n",
"The job s_08_e_m0_02500 was saved and received the ID: 737\n",
"The job s_08_e_0_02500 was saved and received the ID: 738\n",
"The job s_08_e_0_05000 was saved and received the ID: 739\n",
"The job s_23_e_m0_05000 was saved and received the ID: 740\n",
"The job s_23_e_m0_02500 was saved and received the ID: 741\n",
"The job s_23_e_0_02500 was saved and received the ID: 742\n",
"The job s_23_e_0_05000 was saved and received the ID: 743\n",
"The job phonopy_job_Li_bcc was saved and received the ID: 744\n",
"The job ref_job_Li_bcc_0 was saved and received the ID: 745\n",
"The job elastic_job_LiAl_B2 was saved and received the ID: 746\n",
"The job s_e_0 was saved and received the ID: 747\n",
"The job s_01_e_m0_05000 was saved and received the ID: 748\n",
"The job s_01_e_m0_02500 was saved and received the ID: 749\n",
"The job s_01_e_0_02500 was saved and received the ID: 750\n",
"The job s_01_e_0_05000 was saved and received the ID: 751\n",
"The job s_08_e_m0_05000 was saved and received the ID: 752\n",
"The job s_08_e_m0_02500 was saved and received the ID: 753\n",
"The job s_08_e_0_02500 was saved and received the ID: 754\n",
"The job s_08_e_0_05000 was saved and received the ID: 755\n",
"The job s_23_e_m0_05000 was saved and received the ID: 756\n",
"The job s_23_e_m0_02500 was saved and received the ID: 757\n",
"The job s_23_e_0_02500 was saved and received the ID: 758\n",
"The job s_23_e_0_05000 was saved and received the ID: 759\n",
"The job phonopy_job_LiAl_B2 was saved and received the ID: 760\n",
"The job ref_job_LiAl_B2_0 was saved and received the ID: 761\n",
"The job ref_job_LiAl_B2_1 was saved and received the ID: 762\n"
]
}
],
"source": [
"for pot in potentials_list:\n",
" group_name = get_clean_project_name(pot)\n",
" pr_pot = pr.create_group(group_name)\n",
" print(group_name)\n",
" \n",
" for _, row in data_murn[data_murn.potential==group_name].iterrows():\n",
" job_id = row.job_id\n",
" \n",
" job_ref = pr_pot.create_job(pr_pot.job_type.Lammps, f\"ref_job_{row.compound}_{row.crystal_structure}\")\n",
" ref = pr_pot.load(job_id)\n",
" job_ref.structure = ref.structure\n",
" job_ref.potential = pot\n",
" job_ref.calc_minimize()\n",
" elastic_job = job_ref.create_job(pr_pot.job_type.ElasticMatrixJob, f\"elastic_job_{row.compound}_{row.crystal_structure}\")\n",
" elastic_job.input[\"eps_range\"] = 0.05\n",
" elastic_job.run()\n",
" \n",
" \n",
" phonopy_job = job_ref.create_job(pr_pot.job_type.PhonopyJob, f\"phonopy_job_{row.compound}_{row.crystal_structure}\")\n",
" job_ref.calc_static()\n",
" phonopy_job.run()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "a035813c-039d-4981-b3ba-516b40bb3c4d",
"metadata": {},
"outputs": [],
"source": [
"def filter_elastic(job_table):\n",
" return (job_table.hamilton == \"ElasticMatrixJob\") & (job_table.status == \"finished\")\n",
"\n",
"# Get corresponding lattice constants\n",
"def get_c11(job_path):\n",
" return job_path[\"output/elasticmatrix\"][\"C\"][0, 0]\n",
"\n",
"def get_c12(job_path):\n",
" return job_path[\"output/elasticmatrix\"][\"C\"][0, 1]\n",
"\n",
"def get_c44(job_path):\n",
" return job_path[\"output/elasticmatrix\"][\"C\"][3, 3]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "ba95973a-a00f-41a9-b23f-2b4bcf629aaf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The job table_elastic was saved and received the ID: 763\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "050dd2b61d5d4f54846599f2356ff8c3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading and filtering jobs: 0%| | 0/5 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3e8f3c8706c04881a19d1922c21a9bde",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Processing jobs: 0%| | 0/5 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
" <th>potential</th>\n",
" <th>C11</th>\n",
" <th>C12</th>\n",
" <th>C44</th>\n",
" <th>compound</th>\n",
" <th>crystal_structure</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>682</td>\n",
" <td>LiAl_eam</td>\n",
" <td>120.339279</td>\n",
" <td>66.483631</td>\n",
" <td>45.515458</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>698</td>\n",
" <td>LiAl_eam</td>\n",
" <td>19.483136</td>\n",
" <td>80.122147</td>\n",
" <td>56.596288</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>714</td>\n",
" <td>LiAl_eam</td>\n",
" <td>17.071189</td>\n",
" <td>12.323044</td>\n",
" <td>12.893122</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>730</td>\n",
" <td>LiAl_eam</td>\n",
" <td>16.740018</td>\n",
" <td>11.018163</td>\n",
" <td>12.688217</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>746</td>\n",
" <td>LiAl_eam</td>\n",
" <td>67.699915</td>\n",
" <td>68.635697</td>\n",
" <td>53.480973</td>\n",
" <td>LiAl</td>\n",
" <td>B2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential C11 C12 C44 compound \\\n",
"0 682 LiAl_eam 120.339279 66.483631 45.515458 Al \n",
"1 698 LiAl_eam 19.483136 80.122147 56.596288 Al \n",
"2 714 LiAl_eam 17.071189 12.323044 12.893122 Li \n",
"3 730 LiAl_eam 16.740018 11.018163 12.688217 Li \n",
"4 746 LiAl_eam 67.699915 68.635697 53.480973 LiAl \n",
"\n",
" crystal_structure \n",
"0 fcc \n",
"1 bcc \n",
"2 fcc \n",
"3 bcc \n",
"4 B2 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table = pr.create_table(\"table_elastic\", delete_existing_job=True)\n",
"table.db_filter_function = filter_elastic\n",
"table.add[\"potential\"] = get_potential\n",
"table.add[\"C11\"] = get_c11\n",
"table.add[\"C12\"] = get_c12\n",
"table.add[\"C44\"] = get_c44\n",
"table.add[\"compound\"] = get_compound\n",
"table.add[\"crystal_structure\"] = get_crystal_structure\n",
"\n",
"table.run()\n",
"data_elastic = table.get_dataframe()\n",
"data_elastic"
]
},
{
"cell_type": "markdown",
"id": "b33c7fa7-f2a8-4a53-9a03-35a7e4754ac7",
"metadata": {},
"source": [
"### Visualization of the results"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "48e970cb-c28e-436b-a0c5-dcd26f11d41b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
" <th>potential</th>\n",
" <th>compound</th>\n",
" <th>crystal_structure</th>\n",
" <th>a</th>\n",
" <th>eq_vol</th>\n",
" <th>eq_bm</th>\n",
" <th>eq_energy</th>\n",
" <th>n_atoms</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>617</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.039967</td>\n",
" <td>16.495612</td>\n",
" <td>85.876912</td>\n",
" <td>-3.483097</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>630</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.898853</td>\n",
" <td>16.147864</td>\n",
" <td>48.620841</td>\n",
" <td>-3.415312</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>643</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>4.253841</td>\n",
" <td>19.241330</td>\n",
" <td>13.985972</td>\n",
" <td>-1.758107</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>656</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.195477</td>\n",
" <td>20.114514</td>\n",
" <td>13.690609</td>\n",
" <td>-1.757011</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>669</td>\n",
" <td>LiAl_eam</td>\n",
" <td>LiAl</td>\n",
" <td>B2</td>\n",
" <td>4.341228</td>\n",
" <td>28.939499</td>\n",
" <td>71.555056</td>\n",
" <td>-5.300981</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential compound crystal_structure a eq_vol \\\n",
"0 617 LiAl_eam Al fcc 4.039967 16.495612 \n",
"1 630 LiAl_eam Al bcc 3.898853 16.147864 \n",
"2 643 LiAl_eam Li fcc 4.253841 19.241330 \n",
"3 656 LiAl_eam Li bcc 4.195477 20.114514 \n",
"4 669 LiAl_eam LiAl B2 4.341228 28.939499 \n",
"\n",
" eq_bm eq_energy n_atoms \n",
"0 85.876912 -3.483097 1 \n",
"1 48.620841 -3.415312 1 \n",
"2 13.985972 -1.758107 1 \n",
"3 13.690609 -1.757011 1 \n",
"4 71.555056 -5.300981 2 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_murn"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "37bc1ee5-fae8-49da-92bc-a83d1a59e3d2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
" <th>potential</th>\n",
" <th>C11</th>\n",
" <th>C12</th>\n",
" <th>C44</th>\n",
" <th>compound</th>\n",
" <th>crystal_structure</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>682</td>\n",
" <td>LiAl_eam</td>\n",
" <td>120.339279</td>\n",
" <td>66.483631</td>\n",
" <td>45.515458</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>698</td>\n",
" <td>LiAl_eam</td>\n",
" <td>19.483136</td>\n",
" <td>80.122147</td>\n",
" <td>56.596288</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>714</td>\n",
" <td>LiAl_eam</td>\n",
" <td>17.071189</td>\n",
" <td>12.323044</td>\n",
" <td>12.893122</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>730</td>\n",
" <td>LiAl_eam</td>\n",
" <td>16.740018</td>\n",
" <td>11.018163</td>\n",
" <td>12.688217</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>746</td>\n",
" <td>LiAl_eam</td>\n",
" <td>67.699915</td>\n",
" <td>68.635697</td>\n",
" <td>53.480973</td>\n",
" <td>LiAl</td>\n",
" <td>B2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id potential C11 C12 C44 compound \\\n",
"0 682 LiAl_eam 120.339279 66.483631 45.515458 Al \n",
"1 698 LiAl_eam 19.483136 80.122147 56.596288 Al \n",
"2 714 LiAl_eam 17.071189 12.323044 12.893122 Li \n",
"3 730 LiAl_eam 16.740018 11.018163 12.688217 Li \n",
"4 746 LiAl_eam 67.699915 68.635697 53.480973 LiAl \n",
"\n",
" crystal_structure \n",
"0 fcc \n",
"1 bcc \n",
"2 fcc \n",
"3 bcc \n",
"4 B2 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_elastic"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "3773c45f-e7f6-440c-aabc-c0f629a7cf07",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id_murn</th>\n",
" <th>potential</th>\n",
" <th>compound</th>\n",
" <th>crystal_structure</th>\n",
" <th>a</th>\n",
" <th>eq_vol</th>\n",
" <th>eq_bm</th>\n",
" <th>eq_energy</th>\n",
" <th>n_atoms</th>\n",
" <th>job_id_elastic</th>\n",
" <th>C11</th>\n",
" <th>C12</th>\n",
" <th>C44</th>\n",
" <th>phase</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>617</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Al</td>\n",
" <td>fcc</td>\n",
" <td>4.039967</td>\n",
" <td>16.495612</td>\n",
" <td>85.876912</td>\n",
" <td>-3.483097</td>\n",
" <td>1</td>\n",
" <td>682</td>\n",
" <td>120.339279</td>\n",
" <td>66.483631</td>\n",
" <td>45.515458</td>\n",
" <td>Al_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>630</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Al</td>\n",
" <td>bcc</td>\n",
" <td>3.898853</td>\n",
" <td>16.147864</td>\n",
" <td>48.620841</td>\n",
" <td>-3.415312</td>\n",
" <td>1</td>\n",
" <td>698</td>\n",
" <td>19.483136</td>\n",
" <td>80.122147</td>\n",
" <td>56.596288</td>\n",
" <td>Al_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>643</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Li</td>\n",
" <td>fcc</td>\n",
" <td>4.253841</td>\n",
" <td>19.241330</td>\n",
" <td>13.985972</td>\n",
" <td>-1.758107</td>\n",
" <td>1</td>\n",
" <td>714</td>\n",
" <td>17.071189</td>\n",
" <td>12.323044</td>\n",
" <td>12.893122</td>\n",
" <td>Li_fcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>656</td>\n",
" <td>LiAl_eam</td>\n",
" <td>Li</td>\n",
" <td>bcc</td>\n",
" <td>4.195477</td>\n",
" <td>20.114514</td>\n",
" <td>13.690609</td>\n",
" <td>-1.757011</td>\n",
" <td>1</td>\n",
" <td>730</td>\n",
" <td>16.740018</td>\n",
" <td>11.018163</td>\n",
" <td>12.688217</td>\n",
" <td>Li_bcc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>669</td>\n",
" <td>LiAl_eam</td>\n",
" <td>LiAl</td>\n",
" <td>B2</td>\n",
" <td>4.341228</td>\n",
" <td>28.939499</td>\n",
" <td>71.555056</td>\n",
" <td>-5.300981</td>\n",
" <td>2</td>\n",
" <td>746</td>\n",
" <td>67.699915</td>\n",
" <td>68.635697</td>\n",
" <td>53.480973</td>\n",
" <td>LiAl_B2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id_murn potential compound crystal_structure a eq_vol \\\n",
"0 617 LiAl_eam Al fcc 4.039967 16.495612 \n",
"1 630 LiAl_eam Al bcc 3.898853 16.147864 \n",
"2 643 LiAl_eam Li fcc 4.253841 19.241330 \n",
"3 656 LiAl_eam Li bcc 4.195477 20.114514 \n",
"4 669 LiAl_eam LiAl B2 4.341228 28.939499 \n",
"\n",
" eq_bm eq_energy n_atoms job_id_elastic C11 C12 \\\n",
"0 85.876912 -3.483097 1 682 120.339279 66.483631 \n",
"1 48.620841 -3.415312 1 698 19.483136 80.122147 \n",
"2 13.985972 -1.758107 1 714 17.071189 12.323044 \n",
"3 13.690609 -1.757011 1 730 16.740018 11.018163 \n",
"4 71.555056 -5.300981 2 746 67.699915 68.635697 \n",
"\n",
" C44 phase \n",
"0 45.515458 Al_fcc \n",
"1 56.596288 Al_bcc \n",
"2 12.893122 Li_fcc \n",
"3 12.688217 Li_bcc \n",
"4 53.480973 LiAl_B2 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_ground_state = pd.merge(on=[\"potential\", \"compound\", \"crystal_structure\"], left=data_murn, right=data_elastic, suffixes=('_murn', '_elastic'))\n",
"df_ground_state[\"phase\"] = df_ground_state.compound + \"_\" + df_ground_state.crystal_structure\n",
"df_ground_state"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "d7e4c0af-4423-4a06-ace2-0345503990ed",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1440x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax_list = plt.subplots(ncols=len(potentials_list), nrows=1, sharex=\"row\", sharey=\"row\")\n",
"\n",
"fig.set_figwidth(20)\n",
"fig.set_figheight(6)\n",
"\n",
"color_palette = sns.color_palette(\"tab10\", n_colors=len(df_ground_state.phase.unique()))\n",
"\n",
"\n",
"for i, pot in enumerate(potentials_list):\n",
" \n",
" if len(potentials_list) == 1:\n",
" ax = ax_list\n",
" else:\n",
" ax = ax_list[i]\n",
" data = df_ground_state[df_ground_state.potential == get_clean_project_name(pot)]\n",
" \n",
" for j, (_, row) in enumerate(data.iterrows()):\n",
" \n",
" ax = pr.load(row.job_id_murn).plot(plt_show=False, ax=ax, plot_kwargs={\"label\": row.phase, \"color\": color_palette[j]})\n",
" \n",
" ax.set_title(f\"{get_clean_project_name(pot)}\")\n",
" #break\n",
"fig.subplots_adjust(wspace=0.1);"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "c7dce127-f486-4d6e-a7f0-c335330de85c",
"metadata": {},
"outputs": [],
"source": [
"# fig, ax_list = plt.subplots(ncols=len(potential_list), nrows=1, sharex=\"row\", sharey=\"row\")\n",
"\n",
"# fig.set_figwidth(20)\n",
"# fig.set_figheight(6)\n",
"\n",
"# color_palette = sns.color_palette(\"tab10\", n_colors=len(df_ground_state.phase.unique()))\n",
"\n",
"\n",
"# for i, pot in enumerate(potential_list):\n",
" \n",
"# ax = ax_list[i]\n",
"# data = df_ground_state[df_ground_state.potential == clean_project_name(pot)]\n",
" \n",
"# for j, (_, row) in enumerate(data.iterrows()):\n",
" \n",
"# ax = pr.load(row.job_id_murn).plot(plt_show=False, ax=ax, plot_kwargs={\"label\": f\"phase_{j}\", \"color\": color_palette[j]})\n",
" \n",
"# ax.set_title(f\"Potential {i}\")\n",
"# #break\n",
"# fig.subplots_adjust(wspace=0.1);\n",
"# plt.savefig(\"example.jpeg\", bbox_inches=\"tight\");"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "d2f11623-e92b-4a71-8440-d84a1d48392e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x360 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax_list = plt.subplots(ncols=len(df_ground_state.phase.unique()), nrows=1, sharex=\"row\", sharey=\"row\")\n",
"\n",
"fig.set_figwidth(20)\n",
"fig.set_figheight(5)\n",
"\n",
"color_palette = sns.color_palette(\"tab10\", n_colors=len(df_ground_state.potential.unique()))\n",
"\n",
"\n",
"for i, phase in enumerate(df_ground_state.phase.unique()):\n",
" \n",
" ax = ax_list[i]\n",
" data = df_ground_state[df_ground_state.phase == phase]\n",
" \n",
" \n",
" \n",
" for j, pot in enumerate(potentials_list):\n",
" \n",
" phonopy_job = pr[get_clean_project_name(pot) + f\"/phonopy_job_{phase}\"]\n",
" \n",
" thermo = phonopy_job.get_thermal_properties(t_min=0, t_max=800)\n",
"\n",
" ax.plot(thermo.temperatures, thermo.free_energies, label=get_clean_project_name(pot), color=color_palette[j])\n",
" ax.set_xlabel(\"Temperatures [K]\")\n",
" ax.set_title(f\"{phase}\")\n",
"ax_list[0].set_ylabel(\"Free energies [eV]\")\n",
"\n",
"ax_list[-1].legend()\n",
"fig.subplots_adjust(wspace=0.1);"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "b317b1d3-549b-4e0e-84bf-3cd02a92596d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1440x360 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax_list = plt.subplots(ncols=len(df_ground_state.phase.unique()), nrows=1, sharex=\"row\", sharey=\"row\")\n",
"\n",
"fig.set_figwidth(20)\n",
"fig.set_figheight(5)\n",
"\n",
"color_palette = sns.color_palette(\"tab10\", n_colors=len(df_ground_state.potential.unique()))\n",
"\n",
"\n",
"for i, phase in enumerate(df_ground_state.phase.unique()):\n",
" \n",
" ax = ax_list[i]\n",
" data = df_ground_state[df_ground_state.phase == phase]\n",
" \n",
" \n",
" \n",
" for j, pot in enumerate(potentials_list):\n",
" \n",
" phonopy_job = pr[get_clean_project_name(pot) + f\"/phonopy_job_{phase}\"]\n",
" \n",
" thermo = phonopy_job.get_thermal_properties(t_min=0, t_max=800)\n",
" \n",
" ax.plot(phonopy_job[\"output/dos_energies\"], phonopy_job[\"output/dos_total\"], color=color_palette[j], label=get_clean_project_name(pot))\n",
" ax.set_xlabel(\"Frequency [THz]\")\n",
" ax.set_title(f\"{phase}\")\n",
"ax_list[0].set_ylabel(\"DOS\")\n",
"\n",
"ax_list[-1].legend()\n",
"fig.subplots_adjust(wspace=0.1);"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "7c036a6e-0a66-4adf-8a1e-6ce94ad91ee0",
"metadata": {},
"outputs": [],
"source": [
"# phonopy_job.plot_band_structure()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "1c03fae6-b856-47f6-8c5b-043aeb6c488c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1800x864 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax_list = plt.subplots(ncols=len(df_ground_state.phase.unique()), nrows=len(potentials_list), sharey=\"row\")\n",
"\n",
"fig.set_figwidth(25)\n",
"fig.set_figheight(12)\n",
"\n",
"color_palette = sns.color_palette(\"tab10\", n_colors=len(df_ground_state.potential.unique()))\n",
"\n",
"\n",
"for i, phase in enumerate(df_ground_state.phase.unique()):\n",
" \n",
" \n",
" data = df_ground_state[df_ground_state.phase == phase]\n",
" \n",
" \n",
" \n",
" for j, pot in enumerate(potentials_list):\n",
" \n",
" if len(potentials_list) == 1:\n",
" ax = ax_list[i]\n",
" ax_list[0].set_ylabel(\"DOS\")\n",
" ax.set_title(f\"{phase}\")\n",
" \n",
" else:\n",
" ax = ax_list[j][i]\n",
" ax_list[j][0].set_ylabel(\"DOS\")\n",
" ax_list[0][i].set_title(f\"{phase}\")\n",
" phonopy_job = pr[get_clean_project_name(pot) + f\"/phonopy_job_{phase}\"]\n",
" \n",
" phonopy_job.plot_band_structure(axis=ax)\n",
" ax.set_ylabel(\"\")\n",
" ax.set_title(\"\")\n",
" \n",
"fig.subplots_adjust(wspace=0.1, hspace=0.4);"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "61e2a3bc-2bc0-4549-9d09-8075f50cbd8e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total run time for the notebook 355.40625 seconds\n"
]
}
],
"source": [
"time_stop = time.process_time()\n",
"print(f\"Total run time for the notebook {time_stop - time_start} seconds\")"
]
},
{
"cell_type": "markdown",
"id": "8173aa8d-5f79-41f9-9d5f-f1573a415e2a",
"metadata": {},
"source": [
"Todo:\n",
"\n",
" - SQS and intermediate ordered phases, layered phases (supply the structures)\n",
" - Properties of compounds\n",
" - Split the workflows into several notebooks\n",
" - Defect formation energies etc.\n",
" - Link to Sarath's part?? (Thermal expansion using MD/QHA)\n",
" - Showing that MD works with these potentials"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59d98667-c265-4a29-9267-25faeb033471",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}