{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "25d5b0d5-f330-4dcb-9b7c-f57c4bea9596",
   "metadata": {},
   "source": [
    "# Validation of the potentials\n",
    "\n",
    "Once we have the fitted potentials, it is necessary to validate them in order to assess their quality with respect to applications.\n",
    "\n",
    "In this exercise, we use the fitted potentials and perform some basic calculations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4756d4c9-304a-4ccc-b772-ba67d008c5a4",
   "metadata": {},
   "source": [
    "## Import the fitted potentials for Li-Al (from earlier excercise)\n",
    "\n",
    "The same directory contains a `helper.py` file which among other things, also contains the necessary specifications of each of the potentials that we will use today. Individual potentials are descrbed in the LAMMPS format as:\n",
    "```\n",
    "pot_eam = pd.DataFrame({\n",
    "    'Name': ['LiAl_eam'],\n",
    "    'Filename': [[\"../potentials/AlLi.eam.fs\")]],\n",
    "    'Model': [\"EAM\"],\n",
    "    'Species': [['Li', 'Al']],\n",
    "    'Config': [['pair_style eam/fs\\n', 'pair_coeff * * AlLi.eam.fs Li Al\\n']]\n",
    "})\n",
    "\n",
    "```\n",
    "A list of such DataFrames describing the potentials is saved in a list called `potentials_list`. We import the list as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b90e0ac0",
   "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_yace</td>\n",
       "      <td>[/home/jovyan/workshop_preparation/potentials/...</td>\n",
       "      <td>ACE</td>\n",
       "      <td>[Al, Li]</td>\n",
       "      <td>[pair_style pace\\n, pair_coeff * * AlLi-6gen-1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name                                           Filename Model  \\\n",
       "0  LiAl_yace  [/home/jovyan/workshop_preparation/potentials/...   ACE   \n",
       "\n",
       "    Species                                             Config  \n",
       "0  [Al, Li]  [pair_style pace\\n, pair_coeff * * AlLi-6gen-1...  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from helper import potentials_list\n",
    "\n",
    "# potentials_list = [potentials_list[0],potentials_list[1]]\n",
    "\n",
    "# display the first element in the list\n",
    "# which is an EAM potential\n",
    "potentials_list[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c84560c",
   "metadata": {},
   "source": [
    "## Import other important modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83f7a2c9-d45a-4987-9e35-59badd754d4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1654697715.1585205"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "\n",
    "from helper import get_clean_project_name\n",
    "\n",
    "from pyiron_atomistics import Project\n",
    "from pyiron import pyiron_to_ase\n",
    "import pyiron_gpl\n",
    "\n",
    "# save start time to record runtime of the notebook\n",
    "time_start =  time.time()\n",
    "time_start"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acc0ee8f",
   "metadata": {},
   "source": [
    "## Create a new project to perform validation calculations\n",
    "\n",
    "It is useful to create a new project directory for every kind of calculation. Pyiron will automatically create subdirectories for each potential and property we calculate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "706be2a9-5f94-4eb5-8e4f-6c349fe216b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr = Project(\"validation_LiAl\")\n",
    "\n",
    "# remove earlier jobs\n",
    "# pr.remove_jobs(silently=True, recursive=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b84ed62-e841-4526-893e-dc4f61477c88",
   "metadata": {},
   "source": [
    "## Define the important pases to consider for validation\n",
    "\n",
    "We construct a python dictionary `struct_dict` which contains a description of all the important phases that we want to consider for this exercise. The descriptions given in the dictionary will be later used by Pyiron to generate or read the structural configurations for the respective phases.\n",
    "\n",
    "For unary phases, we provide an initial guess for the lattice parameter and use pyiron to generate the structural prototype.\n",
    "\n",
    "For binary phases, we provide a phase name and an additional dictionary `fl_dict` which maps the phase name to a `.cif` file saved in a subdirectory. Pyiron will use this information to read the respective configurations from the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28778cef-2a07-4794-888f-7239500e7b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Al': {'s_murn': ['fcc', 'bcc'], 'a': 4.04},\n",
       " 'Li': {'s_murn': ['bcc', 'fcc'], 'a': 3.5},\n",
       " 'Li2Al2': {'s_murn': ['Li2Al2_cubic']},\n",
       " 'LiAl3': {'s_murn': ['LiAl3_cubic']},\n",
       " 'Li9Al4': {'s_murn': ['Li9Al4_monoclinic']},\n",
       " 'Li3Al2': {'s_murn': ['Li3Al2_trigonal']},\n",
       " 'Li4Al4': {'s_murn': ['Li4Al4_cubic']}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "struct_dict = dict()\n",
    "\n",
    "# structures to be generated automatically\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\"] = [\"bcc\",\"fcc\"]\n",
    "struct_dict[\"Li\"][\"a\"] = 3.5\n",
    "\n",
    "\n",
    "# structures to be read from file\n",
    "struct_dict[\"Li2Al2\"] = dict()\n",
    "struct_dict[\"Li2Al2\"][\"s_murn\"] = [\"Li2Al2_cubic\"]\n",
    "\n",
    "struct_dict[\"LiAl3\"] = dict()\n",
    "struct_dict[\"LiAl3\"][\"s_murn\"] = [\"LiAl3_tetragonal\"]\n",
    "\n",
    "struct_dict[\"LiAl3\"] = dict()\n",
    "struct_dict[\"LiAl3\"][\"s_murn\"] = [\"LiAl3_cubic\"]\n",
    "\n",
    "struct_dict[\"Li9Al4\"] = dict()\n",
    "struct_dict[\"Li9Al4\"][\"s_murn\"] = [\"Li9Al4_monoclinic\"]\n",
    "\n",
    "struct_dict[\"Li3Al2\"] = dict()\n",
    "struct_dict[\"Li3Al2\"][\"s_murn\"] = [\"Li3Al2_trigonal\"]\n",
    "\n",
    "struct_dict[\"Li4Al4\"] = dict()\n",
    "struct_dict[\"Li4Al4\"][\"s_murn\"] = [\"Li4Al4_cubic\"]\n",
    "\n",
    "struct_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b2e6d9",
   "metadata": {},
   "source": [
    "a dictionary is described to map the binary phases to their file locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c1820db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "fl_dict = {\"Li2Al2_cubic\": \"mp_structures/LiAl_mp-1067_primitive.cif\",\n",
    "           \"LiAl3_tetragonal\":\"mp_structures/LiAl3_mp-975906_primitive.cif\",\n",
    "           \"LiAl3_cubic\":\"mp_structures/LiAl3_mp-10890_primitive.cif\",\n",
    "           \"Li9Al4_monoclinic\":\"mp_structures/Li9Al4_mp-568404_primitive.cif\",\n",
    "           \"Li3Al2_trigonal\":\"mp_structures/Al2Li3-6021.cif\",\n",
    "           \"Li4Al4_cubic\":\"mp_structures/LiAl_mp-1079240_primitive.cif\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ccb5eb6-f99f-403b-af30-cf8e15a06bb0",
   "metadata": {},
   "source": [
    "### Visualize the strucs\n",
    "\n",
    "Once the structures are defined in the pyiron format, we can view their atomic coordinates and cell vectors using `struc.plot3d()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5902a7f-88f1-4285-bc9b-1d84baf18eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Option 1: use `ase.build.bulk` functionality in pyiron\n",
    "struc = pr.create_ase_bulk(\"Al\", \"fcc\", a=4.04,cubic=True)\n",
    "\n",
    "# struc.plot3d()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "374692a8-630a-4a5d-8ff9-9218150f9d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Option 2: Read from a file\n",
    "struc = pr.create.structure.ase.read(fl_dict[\"Li4Al4_cubic\"])\n",
    "\n",
    "# struc.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "198e9745-734a-4502-8f1b-0330ba8c8fca",
   "metadata": {},
   "source": [
    "## (a) Ground state: E-V curves\n",
    "\n",
    "Using a series of nested `for` loops, we calculate the murnaghan EV-curves using all three potentials for all the defined structures.\n",
    "\n",
    "We loop over:\n",
    " - All the potentials defined in `potentials_list` and name the project according to the potential\n",
    "   - All the chemical formulae defined in the keys of `struct_dict`\n",
    "     - All phases defined for a given chemical formula\n",
    "     \n",
    "Within the loops, the first step is to get the structure basis on which we will perform the calculations. \n",
    "\n",
    "- For unary phases, we use the pyiron function `pr_pot.create_ase_bulk(compound, crys_structure, a=compound_dict[\"a\"])` \n",
    "- For binary structures, we read the basis using `pr.create.structure.ase.read(fl_path)` with the `fl_path` given by `fl_dict` defined earlier.\n",
    "\n",
    "Once the structure and potential is defined as part of the pr_job, we run two calculations:\n",
    "- `job_relax` to relax the structure to the ground state\n",
    "- `murn_job` to calculate the energies in a small volume range around the equilibrium\n",
    "\n",
    "As the calculations are being performed, the status(s) of each calculation is printed. If a job is already calculated, the calculations are not re-run but rather re-read from the saved data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "13f095d2-44d7-4711-b9a5-d58a95af42f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/workshop_preparation/validation/validation_LiAl/LiAl_eam/\n",
      "The job Al_fcc_relax was saved and received the ID: 1752\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:16,704 - pyiron_log - WARNING - The job murn_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Al_bcc_relax was saved and received the ID: 1753\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:18,339 - pyiron_log - WARNING - The job murn_job_Al_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li_bcc_relax was saved and received the ID: 1754\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:19,051 - pyiron_log - WARNING - The job murn_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li_fcc_relax was saved and received the ID: 1755\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:20,323 - pyiron_log - WARNING - The job murn_job_Li_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li2Al2_Li2Al2_cubic_relax was saved and received the ID: 1756\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:22,012 - pyiron_log - WARNING - The job murn_job_Li2Al2_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job LiAl3_LiAl3_cubic_relax was saved and received the ID: 1757\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:23,393 - pyiron_log - WARNING - The job murn_job_LiAl3_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li9Al4_Li9Al4_monoclinic_relax was saved and received the ID: 1758\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:24,515 - pyiron_log - WARNING - The job murn_job_Li9Al4_Li9Al4_monoclinic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li3Al2_Li3Al2_trigonal_relax was saved and received the ID: 1759\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:25,720 - pyiron_log - WARNING - The job murn_job_Li3Al2_Li3Al2_trigonal is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li4Al4_Li4Al4_cubic_relax was saved and received the ID: 1760\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:27,012 - pyiron_log - WARNING - The job murn_job_Li4Al4_Li4Al4_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/workshop_preparation/validation/validation_LiAl/RuNNer-AlLi/\n",
      "The job Al_fcc_relax was saved and received the ID: 1761\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:27,799 - pyiron_log - WARNING - The job murn_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Al_bcc_relax was saved and received the ID: 1762\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:28,779 - pyiron_log - WARNING - The job murn_job_Al_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li_bcc_relax was saved and received the ID: 1763\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:29,535 - pyiron_log - WARNING - The job murn_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li_fcc_relax was saved and received the ID: 1764\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:30,637 - pyiron_log - WARNING - The job murn_job_Li_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li2Al2_Li2Al2_cubic_relax was saved and received the ID: 1765\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:32,209 - pyiron_log - WARNING - The job murn_job_Li2Al2_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job LiAl3_LiAl3_cubic_relax was saved and received the ID: 1766\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:33,439 - pyiron_log - WARNING - The job murn_job_LiAl3_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li9Al4_Li9Al4_monoclinic_relax was saved and received the ID: 1767\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:34,543 - pyiron_log - WARNING - The job murn_job_Li9Al4_Li9Al4_monoclinic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li3Al2_Li3Al2_trigonal_relax was saved and received the ID: 1768\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:35,739 - pyiron_log - WARNING - The job murn_job_Li3Al2_Li3Al2_trigonal is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li4Al4_Li4Al4_cubic_relax was saved and received the ID: 1769\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:36,881 - pyiron_log - WARNING - The job murn_job_Li4Al4_Li4Al4_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/workshop_preparation/validation/validation_LiAl/LiAl_yace/\n",
      "The job Al_fcc_relax was saved and received the ID: 1770\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:38,178 - pyiron_log - WARNING - The job murn_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Al_bcc_relax was saved and received the ID: 1771\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:39,859 - pyiron_log - WARNING - The job murn_job_Al_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li_bcc_relax was saved and received the ID: 1772\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:41,447 - pyiron_log - WARNING - The job murn_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li_fcc_relax was saved and received the ID: 1773\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:42,997 - pyiron_log - WARNING - The job murn_job_Li_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li2Al2_Li2Al2_cubic_relax was saved and received the ID: 1774\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:44,100 - pyiron_log - WARNING - The job murn_job_Li2Al2_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job LiAl3_LiAl3_cubic_relax was saved and received the ID: 1775\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:45,161 - pyiron_log - WARNING - The job murn_job_LiAl3_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li9Al4_Li9Al4_monoclinic_relax was saved and received the ID: 1776\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:46,678 - pyiron_log - WARNING - The job murn_job_Li9Al4_Li9Al4_monoclinic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li3Al2_Li3Al2_trigonal_relax was saved and received the ID: 1777\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:48,152 - pyiron_log - WARNING - The job murn_job_Li3Al2_Li3Al2_trigonal is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Li4Al4_Li4Al4_cubic_relax was saved and received the ID: 1778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:15:49,387 - pyiron_log - WARNING - The job murn_job_Li4Al4_Li4Al4_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    }
   ],
   "source": [
    "for pot in potentials_list:\n",
    "    with pr.open(get_clean_project_name(pot)) as pr_pot:\n",
    "        print(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 in [\"fcc\",\"bcc\"]:\n",
    "                    basis = pr_pot.create_ase_bulk(compound, crys_structure, a=compound_dict[\"a\"])\n",
    "                else:\n",
    "                    basis = pr_pot.create.structure.ase.read(fl_dict[crys_structure])\n",
    "                    \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",
    "                \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": "markdown",
   "id": "9d848f1a",
   "metadata": {},
   "source": [
    "One can display the technical details of all submitted jobs using `pr.job_table()` below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fdc89ebb-3c2a-4315-8fe0-3ae470375223",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# pr.job_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "425dcaec",
   "metadata": {},
   "source": [
    "In order to get read useful results from the completed calculations (eq_energy, eq_volume, etc), it is useful to define the following functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef2f414b-64b8-49aa-87e9-e204950da938",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    "def get_ase_atoms(job_path):\n",
    "    return pyiron_to_ase(job_path.structure).copy()\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": "markdown",
   "id": "2fe57b8b",
   "metadata": {},
   "source": [
    "Using the functions defined above, one can now define a `pd.DataFrame` containing all useful results"
   ]
  },
  {
   "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: 1779\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e14e271016bb4acd89c1fd1d4a475704",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading and filtering jobs:   0%|          | 0/27 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f8fdcefa33941dcb7d235877eaaf96b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing jobs:   0%|          | 0/27 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/pyiron_base/table/datamining.py:620: PerformanceWarning: \n",
      "your performance may suffer as PyTables will pickle object types that it cannot\n",
      "map directly to c-types [inferred_type->mixed,key->block2_values] [items->Index(['potential', 'ase_atoms', 'compound', 'crystal_structure'], dtype='object')]\n",
      "\n",
      "  self.pyiron_table._df.to_hdf(\n"
     ]
    },
    {
     "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>ase_atoms</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>phase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1140</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</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>Al_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1153</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</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>Al_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1166</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</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>Li_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1179</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</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>Li_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1192</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12...</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.165940</td>\n",
       "      <td>58.604895</td>\n",
       "      <td>100.347240</td>\n",
       "      <td>-11.074362</td>\n",
       "      <td>4</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1205</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760...</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>5.607502</td>\n",
       "      <td>62.227580</td>\n",
       "      <td>51.472656</td>\n",
       "      <td>-12.774590</td>\n",
       "      <td>4</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1218</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [4.9874611628416465, 1.0099045365192156, 0.8188840806477526], index=0), Atom('Li', [3.1237816780987666, 1.455730745331952, 2.673723152073369], index=1), Atom('Li', [-3.4421956688209843...</td>\n",
       "      <td>Li9Al4</td>\n",
       "      <td>monoclinic</td>\n",
       "      <td>13.023701</td>\n",
       "      <td>190.504374</td>\n",
       "      <td>53.125276</td>\n",
       "      <td>-28.970054</td>\n",
       "      <td>13</td>\n",
       "      <td>Li9Al4_monoclinic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1231</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Al', [2.1548001975659234, 1.244075358781918, 1.861784175000869], index=0), Atom('Al', [-2.154798282819334, 3.732223313213554, 2.6646760238080542], index=1), Atom('Li', [8.560563403365654e-0...</td>\n",
       "      <td>Li3Al2</td>\n",
       "      <td>trigonal</td>\n",
       "      <td>6.094693</td>\n",
       "      <td>72.810229</td>\n",
       "      <td>69.231669</td>\n",
       "      <td>-12.413856</td>\n",
       "      <td>5</td>\n",
       "      <td>Li3Al2_trigonal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1244</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [2.142967147985671, 1.2372426587287435, 7.662120717536293], index=0), Atom('Li', [-8.783761113500244e-10, 2.4744853189563414, 0.5913679335098909], index=1), Atom('Li', [-8.783761113500...</td>\n",
       "      <td>Li4Al4</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.061226</td>\n",
       "      <td>131.389799</td>\n",
       "      <td>71.221355</td>\n",
       "      <td>-20.506570</td>\n",
       "      <td>8</td>\n",
       "      <td>Li4Al4_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1257</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Al</td>\n",
       "      <td>fcc</td>\n",
       "      <td>4.025259</td>\n",
       "      <td>16.355737</td>\n",
       "      <td>76.669339</td>\n",
       "      <td>-3.484016</td>\n",
       "      <td>1</td>\n",
       "      <td>Al_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1270</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Al</td>\n",
       "      <td>bcc</td>\n",
       "      <td>3.958447</td>\n",
       "      <td>16.870137</td>\n",
       "      <td>51.052272</td>\n",
       "      <td>-3.432183</td>\n",
       "      <td>1</td>\n",
       "      <td>Al_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1283</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Li</td>\n",
       "      <td>bcc</td>\n",
       "      <td>4.211118</td>\n",
       "      <td>20.286595</td>\n",
       "      <td>8.517306</td>\n",
       "      <td>-1.755918</td>\n",
       "      <td>1</td>\n",
       "      <td>Li_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1296</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Li</td>\n",
       "      <td>fcc</td>\n",
       "      <td>3.967043</td>\n",
       "      <td>15.678901</td>\n",
       "      <td>147.215464</td>\n",
       "      <td>-1.769260</td>\n",
       "      <td>1</td>\n",
       "      <td>Li_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1309</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12...</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.376805</td>\n",
       "      <td>64.816143</td>\n",
       "      <td>57.934650</td>\n",
       "      <td>-11.212634</td>\n",
       "      <td>4</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1322</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998...</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>5.700455</td>\n",
       "      <td>65.403086</td>\n",
       "      <td>59.308440</td>\n",
       "      <td>-12.574696</td>\n",
       "      <td>4</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1335</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [5.206051477294367, 1.0619663179427192, 0.8311820920214751], index=0), Atom('Li', [3.28638171437237, 1.5211864250363467, 2.7226207058417775], index=1), Atom('Li', [-3.6198784902055765,...</td>\n",
       "      <td>Li9Al4</td>\n",
       "      <td>monoclinic</td>\n",
       "      <td>13.640614</td>\n",
       "      <td>218.932018</td>\n",
       "      <td>33.874957</td>\n",
       "      <td>-31.820765</td>\n",
       "      <td>13</td>\n",
       "      <td>Li9Al4_monoclinic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1348</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Al', [2.2338755345732753, 1.289729472183878, 1.9126243306628208], index=0), Atom('Al', [-2.233873547699001, 3.869185551846968, 2.7799443936883206], index=1), Atom('Li', [9.007133262260959e-...</td>\n",
       "      <td>Li3Al2</td>\n",
       "      <td>trigonal</td>\n",
       "      <td>6.318351</td>\n",
       "      <td>81.143544</td>\n",
       "      <td>44.574696</td>\n",
       "      <td>-13.185198</td>\n",
       "      <td>5</td>\n",
       "      <td>Li3Al2_trigonal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1361</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [2.220260976080854, 1.2818682724036983, 7.872085429446316], index=0), Atom('Li', [1.722758777253687e-10, 2.5637365444716322, 0.6790950189344616], index=1), Atom('Li', [1.72275877725368...</td>\n",
       "      <td>Li4Al4</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.279846</td>\n",
       "      <td>146.014891</td>\n",
       "      <td>37.664442</td>\n",
       "      <td>-21.680919</td>\n",
       "      <td>8</td>\n",
       "      <td>Li4Al4_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1393</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Al</td>\n",
       "      <td>fcc</td>\n",
       "      <td>4.044553</td>\n",
       "      <td>16.541594</td>\n",
       "      <td>87.130427</td>\n",
       "      <td>-3.478909</td>\n",
       "      <td>1</td>\n",
       "      <td>Al_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1406</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Al</td>\n",
       "      <td>bcc</td>\n",
       "      <td>3.953036</td>\n",
       "      <td>16.811334</td>\n",
       "      <td>72.667242</td>\n",
       "      <td>-3.388831</td>\n",
       "      <td>1</td>\n",
       "      <td>Al_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1419</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Li</td>\n",
       "      <td>bcc</td>\n",
       "      <td>4.216389</td>\n",
       "      <td>20.403222</td>\n",
       "      <td>15.823747</td>\n",
       "      <td>-1.756104</td>\n",
       "      <td>1</td>\n",
       "      <td>Li_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1435</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Li</td>\n",
       "      <td>fcc</td>\n",
       "      <td>4.331457</td>\n",
       "      <td>20.318983</td>\n",
       "      <td>14.231625</td>\n",
       "      <td>-1.755594</td>\n",
       "      <td>1</td>\n",
       "      <td>Li_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1451</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e...</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.367064</td>\n",
       "      <td>64.521799</td>\n",
       "      <td>46.107162</td>\n",
       "      <td>-11.185880</td>\n",
       "      <td>4</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1464</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993...</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>5.686989</td>\n",
       "      <td>65.028366</td>\n",
       "      <td>66.254925</td>\n",
       "      <td>-12.569153</td>\n",
       "      <td>4</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1480</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [5.141009159558869, 1.0571139195527752, 0.820249453790277], index=0), Atom('Li', [3.2705789348169056, 1.5045550288016276, 2.715159327393234], index=1), Atom('Li', [-3.601125467999465, ...</td>\n",
       "      <td>Li9Al4</td>\n",
       "      <td>monoclinic</td>\n",
       "      <td>13.519944</td>\n",
       "      <td>213.136118</td>\n",
       "      <td>33.963240</td>\n",
       "      <td>-31.796316</td>\n",
       "      <td>13</td>\n",
       "      <td>Li9Al4_monoclinic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1493</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Al', [2.2270976540671734, 1.2858164055924044, 1.9025646270076813], index=0), Atom('Al', [-2.227095628822777, 3.8574462424884515, 2.7757665665986657], index=1), Atom('Li', [8.407589514518869...</td>\n",
       "      <td>Li3Al2</td>\n",
       "      <td>trigonal</td>\n",
       "      <td>6.299181</td>\n",
       "      <td>80.375104</td>\n",
       "      <td>39.643133</td>\n",
       "      <td>-13.138303</td>\n",
       "      <td>5</td>\n",
       "      <td>Li3Al2_trigonal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1506</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [2.2269869888586107, 1.285751535686306, 7.864026721150146], index=0), Atom('Li', [-1.5554058443124377e-09, 2.571503074062492, 0.7130584901440213], index=1), Atom('Li', [-1.555405844312...</td>\n",
       "      <td>Li4Al4</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.298870</td>\n",
       "      <td>147.356944</td>\n",
       "      <td>46.701117</td>\n",
       "      <td>-21.607231</td>\n",
       "      <td>8</td>\n",
       "      <td>Li4Al4_cubic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    job_id    potential  \\\n",
       "0     1140     LiAl_eam   \n",
       "1     1153     LiAl_eam   \n",
       "2     1166     LiAl_eam   \n",
       "3     1179     LiAl_eam   \n",
       "4     1192     LiAl_eam   \n",
       "5     1205     LiAl_eam   \n",
       "6     1218     LiAl_eam   \n",
       "7     1231     LiAl_eam   \n",
       "8     1244     LiAl_eam   \n",
       "9     1257  RuNNer-AlLi   \n",
       "10    1270  RuNNer-AlLi   \n",
       "11    1283  RuNNer-AlLi   \n",
       "12    1296  RuNNer-AlLi   \n",
       "13    1309  RuNNer-AlLi   \n",
       "14    1322  RuNNer-AlLi   \n",
       "15    1335  RuNNer-AlLi   \n",
       "16    1348  RuNNer-AlLi   \n",
       "17    1361  RuNNer-AlLi   \n",
       "18    1393    LiAl_yace   \n",
       "19    1406    LiAl_yace   \n",
       "20    1419    LiAl_yace   \n",
       "21    1435    LiAl_yace   \n",
       "22    1451    LiAl_yace   \n",
       "23    1464    LiAl_yace   \n",
       "24    1480    LiAl_yace   \n",
       "25    1493    LiAl_yace   \n",
       "26    1506    LiAl_yace   \n",
       "\n",
       "                                                                                                                                                                                                  ase_atoms  \\\n",
       "0                                                                                                                                                                    (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "1                                                                                                                                                                    (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "2                                                                                                                                                                    (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "3                                                                                                                                                                    (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "4   (Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12...   \n",
       "5   (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760...   \n",
       "6   (Atom('Li', [4.9874611628416465, 1.0099045365192156, 0.8188840806477526], index=0), Atom('Li', [3.1237816780987666, 1.455730745331952, 2.673723152073369], index=1), Atom('Li', [-3.4421956688209843...   \n",
       "7   (Atom('Al', [2.1548001975659234, 1.244075358781918, 1.861784175000869], index=0), Atom('Al', [-2.154798282819334, 3.732223313213554, 2.6646760238080542], index=1), Atom('Li', [8.560563403365654e-0...   \n",
       "8   (Atom('Li', [2.142967147985671, 1.2372426587287435, 7.662120717536293], index=0), Atom('Li', [-8.783761113500244e-10, 2.4744853189563414, 0.5913679335098909], index=1), Atom('Li', [-8.783761113500...   \n",
       "9                                                                                                                                                                    (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "10                                                                                                                                                                   (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "11                                                                                                                                                                   (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "12                                                                                                                                                                   (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "13  (Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12...   \n",
       "14  (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998...   \n",
       "15  (Atom('Li', [5.206051477294367, 1.0619663179427192, 0.8311820920214751], index=0), Atom('Li', [3.28638171437237, 1.5211864250363467, 2.7226207058417775], index=1), Atom('Li', [-3.6198784902055765,...   \n",
       "16  (Atom('Al', [2.2338755345732753, 1.289729472183878, 1.9126243306628208], index=0), Atom('Al', [-2.233873547699001, 3.869185551846968, 2.7799443936883206], index=1), Atom('Li', [9.007133262260959e-...   \n",
       "17  (Atom('Li', [2.220260976080854, 1.2818682724036983, 7.872085429446316], index=0), Atom('Li', [1.722758777253687e-10, 2.5637365444716322, 0.6790950189344616], index=1), Atom('Li', [1.72275877725368...   \n",
       "18                                                                                                                                                                   (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "19                                                                                                                                                                   (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "20                                                                                                                                                                   (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "21                                                                                                                                                                   (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "22  (Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e...   \n",
       "23  (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993...   \n",
       "24  (Atom('Li', [5.141009159558869, 1.0571139195527752, 0.820249453790277], index=0), Atom('Li', [3.2705789348169056, 1.5045550288016276, 2.715159327393234], index=1), Atom('Li', [-3.601125467999465, ...   \n",
       "25  (Atom('Al', [2.2270976540671734, 1.2858164055924044, 1.9025646270076813], index=0), Atom('Al', [-2.227095628822777, 3.8574462424884515, 2.7757665665986657], index=1), Atom('Li', [8.407589514518869...   \n",
       "26  (Atom('Li', [2.2269869888586107, 1.285751535686306, 7.864026721150146], index=0), Atom('Li', [-1.5554058443124377e-09, 2.571503074062492, 0.7130584901440213], index=1), Atom('Li', [-1.555405844312...   \n",
       "\n",
       "   compound crystal_structure          a      eq_vol       eq_bm  eq_energy  \\\n",
       "0        Al               fcc   4.039967   16.495612   85.876912  -3.483097   \n",
       "1        Al               bcc   3.898853   16.147864   48.620841  -3.415312   \n",
       "2        Li               bcc   4.195477   20.114514   13.690609  -1.757011   \n",
       "3        Li               fcc   4.253841   19.241330   13.985972  -1.758107   \n",
       "4    Li2Al2             cubic   6.165940   58.604895  100.347240 -11.074362   \n",
       "5     LiAl3             cubic   5.607502   62.227580   51.472656 -12.774590   \n",
       "6    Li9Al4        monoclinic  13.023701  190.504374   53.125276 -28.970054   \n",
       "7    Li3Al2          trigonal   6.094693   72.810229   69.231669 -12.413856   \n",
       "8    Li4Al4             cubic   6.061226  131.389799   71.221355 -20.506570   \n",
       "9        Al               fcc   4.025259   16.355737   76.669339  -3.484016   \n",
       "10       Al               bcc   3.958447   16.870137   51.052272  -3.432183   \n",
       "11       Li               bcc   4.211118   20.286595    8.517306  -1.755918   \n",
       "12       Li               fcc   3.967043   15.678901  147.215464  -1.769260   \n",
       "13   Li2Al2             cubic   6.376805   64.816143   57.934650 -11.212634   \n",
       "14    LiAl3             cubic   5.700455   65.403086   59.308440 -12.574696   \n",
       "15   Li9Al4        monoclinic  13.640614  218.932018   33.874957 -31.820765   \n",
       "16   Li3Al2          trigonal   6.318351   81.143544   44.574696 -13.185198   \n",
       "17   Li4Al4             cubic   6.279846  146.014891   37.664442 -21.680919   \n",
       "18       Al               fcc   4.044553   16.541594   87.130427  -3.478909   \n",
       "19       Al               bcc   3.953036   16.811334   72.667242  -3.388831   \n",
       "20       Li               bcc   4.216389   20.403222   15.823747  -1.756104   \n",
       "21       Li               fcc   4.331457   20.318983   14.231625  -1.755594   \n",
       "22   Li2Al2             cubic   6.367064   64.521799   46.107162 -11.185880   \n",
       "23    LiAl3             cubic   5.686989   65.028366   66.254925 -12.569153   \n",
       "24   Li9Al4        monoclinic  13.519944  213.136118   33.963240 -31.796316   \n",
       "25   Li3Al2          trigonal   6.299181   80.375104   39.643133 -13.138303   \n",
       "26   Li4Al4             cubic   6.298870  147.356944   46.701117 -21.607231   \n",
       "\n",
       "    n_atoms              phase  \n",
       "0         1             Al_fcc  \n",
       "1         1             Al_bcc  \n",
       "2         1             Li_bcc  \n",
       "3         1             Li_fcc  \n",
       "4         4       Li2Al2_cubic  \n",
       "5         4        LiAl3_cubic  \n",
       "6        13  Li9Al4_monoclinic  \n",
       "7         5    Li3Al2_trigonal  \n",
       "8         8       Li4Al4_cubic  \n",
       "9         1             Al_fcc  \n",
       "10        1             Al_bcc  \n",
       "11        1             Li_bcc  \n",
       "12        1             Li_fcc  \n",
       "13        4       Li2Al2_cubic  \n",
       "14        4        LiAl3_cubic  \n",
       "15       13  Li9Al4_monoclinic  \n",
       "16        5    Li3Al2_trigonal  \n",
       "17        8       Li4Al4_cubic  \n",
       "18        1             Al_fcc  \n",
       "19        1             Al_bcc  \n",
       "20        1             Li_bcc  \n",
       "21        1             Li_fcc  \n",
       "22        4       Li2Al2_cubic  \n",
       "23        4        LiAl3_cubic  \n",
       "24       13  Li9Al4_monoclinic  \n",
       "25        5    Li3Al2_trigonal  \n",
       "26        8       Li4Al4_cubic  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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[\"ase_atoms\"] = get_ase_atoms\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",
    "\n",
    "data_murn = table.get_dataframe()\n",
    "data_murn[\"phase\"] = data_murn.compound + \"_\" + data_murn.crystal_structure\n",
    "data_murn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a21ae117-4270-4a67-94f7-d4f557797dc2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "df_dft_ref = pd.read_pickle(\"dft_ref.pckl\")\n",
    "\n",
    "al_fcc = df_dft_ref[df_dft_ref[\"compound\"]==\"Al_fcc\"]\n",
    "li = df_dft_ref[df_dft_ref[\"compound\"].isin([\"Li_bcc\",\"Li_fcc\"])]\n",
    "df_mixed = df_dft_ref[df_dft_ref[\"compound\"].isin([\"LiAl_mp-1067\",\"LiAl3_mp-10890\",\"Li9Al4_mp-568404\",\"Li3Al2_mp-16506\",\"LiAl_mp-1079240\"])]\n",
    "\n",
    "li[\"energy_per_atom\"] = li[\"energy\"]/li[\"number_of_atoms\"]\n",
    "# li"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "30d27d75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax_list = plt.subplots(ncols=3, nrows=len(potentials_list), sharex=\"col\")\n",
    "\n",
    "fig.set_figwidth(24)\n",
    "fig.set_figheight(20)\n",
    "\n",
    "color_palette = sns.color_palette(\"tab10\", n_colors=len(data_murn.phase.unique()))\n",
    "\n",
    "\n",
    "for i, pot in enumerate(potentials_list):\n",
    "    \n",
    "    \n",
    "    mask1 = data_murn[\"compound\"]==\"Al\"\n",
    "    data1 = data_murn[(data_murn.potential == get_clean_project_name(pot)) & (mask1)]\n",
    "    \n",
    "    mask2 = data_murn[\"compound\"]==\"Li\"\n",
    "    data2 = data_murn[(data_murn.potential == get_clean_project_name(pot)) & (mask2)]\n",
    "    \n",
    "    mask3 = data_murn[\"compound\"].isin([\"Al\",\"Li\"])\n",
    "    data3 = data_murn[(data_murn.potential == get_clean_project_name(pot)) & (~mask3)]\n",
    "\n",
    "    for j,(_, row) in enumerate(data1.iterrows()):\n",
    "        murn_job = pr.load(row[\"job_id\"])\n",
    "        murn_df = murn_job.output_to_pandas()\n",
    "        n_atoms = row[\"n_atoms\"]\n",
    "\n",
    "        ax_list[i,0].plot(murn_df[\"volume\"]/n_atoms, murn_df[\"energy\"]/n_atoms,\"-\",\n",
    "                            lw=4,\n",
    "                            label= row[\"phase\"], \n",
    "                            color=color_palette[j])\n",
    "        \n",
    "        \n",
    "        \n",
    "        ax_list[i,0].set_title(f\"{get_clean_project_name(pot)}\" + '_' + data1.iloc[0][\"compound\"],fontsize=22)\n",
    "        ax_list[i,0].legend(prop={\"size\":16})\n",
    "        \n",
    "    ax_list[i,0].scatter(al_fcc[\"vol\"],al_fcc[\"energy\"]/al_fcc[\"number_of_atoms\"],\n",
    "                            facecolor=\"none\",edgecolor=\"k\",s=100,label=\"DFT\")\n",
    "        \n",
    "    for j,(_, row) in enumerate(data2.iterrows()):\n",
    "        murn_job = pr.load(row[\"job_id\"])\n",
    "        murn_df = murn_job.output_to_pandas()\n",
    "        n_atoms = row[\"n_atoms\"]\n",
    "        \n",
    "        ax_list[i,2].plot(murn_df[\"volume\"]/n_atoms, murn_df[\"energy\"]/n_atoms,\"-\",\n",
    "                            lw=4,\n",
    "                            label= row[\"phase\"], \n",
    "                            color=color_palette[j])\n",
    "        \n",
    "        \n",
    "        \n",
    "        ax_list[i,2].set_title(f\"{get_clean_project_name(pot)}\" + '_' + data2.iloc[0][\"compound\"],fontsize=22)\n",
    "        # ax_list[i,2].legend(prop={\"size\":16})\n",
    "        \n",
    "    ax_list[i,2].scatter(li[\"vol\"],li[\"energy\"]/li[\"number_of_atoms\"],\n",
    "                            facecolor=\"none\",edgecolor=\"k\",s=100,label=\"DFT\")\n",
    "        \n",
    "    for j,(_, row) in enumerate(data3.iterrows()):\n",
    "        murn_job = pr.load(row[\"job_id\"])\n",
    "        murn_df = murn_job.output_to_pandas()\n",
    "        n_atoms = row[\"n_atoms\"]\n",
    "        \n",
    "        ax_list[i,1].plot(murn_df[\"volume\"]/n_atoms, murn_df[\"energy\"]/n_atoms,\"-\",\n",
    "                            lw=4,\n",
    "                            label= row[\"phase\"], \n",
    "                            color=color_palette[j])\n",
    "        \n",
    "        ax_list[i,1].set_title(f\"{get_clean_project_name(pot)}\" + '_AlLi_mixed',fontsize=22)\n",
    "        # ax_list[i,1].legend(prop={\"size\":16})\n",
    "        \n",
    "    ax_list[i,1].scatter(df_mixed[\"vol\"],df_mixed[\"energy\"]/df_mixed[\"number_of_atoms\"],\n",
    "                            facecolor=\"none\",edgecolor=\"k\",s=100,label=\"DFT\")\n",
    "        \n",
    "        \n",
    "for i in range(3):\n",
    "    ax_list[0,i].legend(prop={\"size\":16})\n",
    "    ax_list[-1,i].set_xlabel(\"Volume per atom, $\\mathrm{\\AA^3}$\",fontsize=20)\n",
    "    ax_list[-1,i].tick_params(axis=\"x\",labelsize=18)\n",
    "    \n",
    "for i in range(len(potentials_list)):\n",
    "    ax_list[i,0].set_ylabel(\"Energy per atom, eV/atom\",fontsize=18)\n",
    "    \n",
    "    \n",
    "    \n",
    "# ax.legend(prop={\"size\":16})\n",
    "# ax.set_ylabel(\"Energy per atom, eV/atom\",fontsize=20)\n",
    "#break\n",
    "fig.subplots_adjust(wspace=0.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba90359-a2a5-4f83-9fa8-6dc4d87f5743",
   "metadata": {},
   "source": [
    "## (b) Elastic constants and Phonons\n",
    "\n",
    "Pyiron also has job modules to calculate elastic constants and thermal properties using the quasi-harmonic approximation given by the `phonopy` package.\n",
    "\n",
    "As in the previous task, we again loop over the defined potentials and then over the given structures.\n",
    "\n",
    "Calculating elastic constants and thermal properties is considerably more expensive than calculating EV curves. Hence, it is useful to only calculate these properties for a subset of most important structures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7bf87f90",
   "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>ase_atoms</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>phase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1140</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</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>Al_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1166</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</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>Li_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1192</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12...</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.165940</td>\n",
       "      <td>58.604895</td>\n",
       "      <td>100.347240</td>\n",
       "      <td>-11.074362</td>\n",
       "      <td>4</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1205</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760...</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>5.607502</td>\n",
       "      <td>62.227580</td>\n",
       "      <td>51.472656</td>\n",
       "      <td>-12.774590</td>\n",
       "      <td>4</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1257</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Al</td>\n",
       "      <td>fcc</td>\n",
       "      <td>4.025259</td>\n",
       "      <td>16.355737</td>\n",
       "      <td>76.669339</td>\n",
       "      <td>-3.484016</td>\n",
       "      <td>1</td>\n",
       "      <td>Al_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1283</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Li</td>\n",
       "      <td>bcc</td>\n",
       "      <td>4.211118</td>\n",
       "      <td>20.286595</td>\n",
       "      <td>8.517306</td>\n",
       "      <td>-1.755918</td>\n",
       "      <td>1</td>\n",
       "      <td>Li_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1309</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12...</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.376805</td>\n",
       "      <td>64.816143</td>\n",
       "      <td>57.934650</td>\n",
       "      <td>-11.212634</td>\n",
       "      <td>4</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1322</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998...</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>5.700455</td>\n",
       "      <td>65.403086</td>\n",
       "      <td>59.308440</td>\n",
       "      <td>-12.574696</td>\n",
       "      <td>4</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1393</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Al</td>\n",
       "      <td>fcc</td>\n",
       "      <td>4.044553</td>\n",
       "      <td>16.541594</td>\n",
       "      <td>87.130427</td>\n",
       "      <td>-3.478909</td>\n",
       "      <td>1</td>\n",
       "      <td>Al_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1419</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Li</td>\n",
       "      <td>bcc</td>\n",
       "      <td>4.216389</td>\n",
       "      <td>20.403222</td>\n",
       "      <td>15.823747</td>\n",
       "      <td>-1.756104</td>\n",
       "      <td>1</td>\n",
       "      <td>Li_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1451</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e...</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.367064</td>\n",
       "      <td>64.521799</td>\n",
       "      <td>46.107162</td>\n",
       "      <td>-11.185880</td>\n",
       "      <td>4</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1464</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993...</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>5.686989</td>\n",
       "      <td>65.028366</td>\n",
       "      <td>66.254925</td>\n",
       "      <td>-12.569153</td>\n",
       "      <td>4</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    job_id    potential  \\\n",
       "0     1140     LiAl_eam   \n",
       "2     1166     LiAl_eam   \n",
       "4     1192     LiAl_eam   \n",
       "5     1205     LiAl_eam   \n",
       "9     1257  RuNNer-AlLi   \n",
       "11    1283  RuNNer-AlLi   \n",
       "13    1309  RuNNer-AlLi   \n",
       "14    1322  RuNNer-AlLi   \n",
       "18    1393    LiAl_yace   \n",
       "20    1419    LiAl_yace   \n",
       "22    1451    LiAl_yace   \n",
       "23    1464    LiAl_yace   \n",
       "\n",
       "                                                                                                                                                                                                  ase_atoms  \\\n",
       "0                                                                                                                                                                    (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "2                                                                                                                                                                    (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "4   (Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12...   \n",
       "5   (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760...   \n",
       "9                                                                                                                                                                    (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "11                                                                                                                                                                   (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "13  (Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12...   \n",
       "14  (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998...   \n",
       "18                                                                                                                                                                   (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "20                                                                                                                                                                   (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "22  (Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e...   \n",
       "23  (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993...   \n",
       "\n",
       "   compound crystal_structure         a     eq_vol       eq_bm  eq_energy  \\\n",
       "0        Al               fcc  4.039967  16.495612   85.876912  -3.483097   \n",
       "2        Li               bcc  4.195477  20.114514   13.690609  -1.757011   \n",
       "4    Li2Al2             cubic  6.165940  58.604895  100.347240 -11.074362   \n",
       "5     LiAl3             cubic  5.607502  62.227580   51.472656 -12.774590   \n",
       "9        Al               fcc  4.025259  16.355737   76.669339  -3.484016   \n",
       "11       Li               bcc  4.211118  20.286595    8.517306  -1.755918   \n",
       "13   Li2Al2             cubic  6.376805  64.816143   57.934650 -11.212634   \n",
       "14    LiAl3             cubic  5.700455  65.403086   59.308440 -12.574696   \n",
       "18       Al               fcc  4.044553  16.541594   87.130427  -3.478909   \n",
       "20       Li               bcc  4.216389  20.403222   15.823747  -1.756104   \n",
       "22   Li2Al2             cubic  6.367064  64.521799   46.107162 -11.185880   \n",
       "23    LiAl3             cubic  5.686989  65.028366   66.254925 -12.569153   \n",
       "\n",
       "    n_atoms         phase  \n",
       "0         1        Al_fcc  \n",
       "2         1        Li_bcc  \n",
       "4         4  Li2Al2_cubic  \n",
       "5         4   LiAl3_cubic  \n",
       "9         1        Al_fcc  \n",
       "11        1        Li_bcc  \n",
       "13        4  Li2Al2_cubic  \n",
       "14        4   LiAl3_cubic  \n",
       "18        1        Al_fcc  \n",
       "20        1        Li_bcc  \n",
       "22        4  Li2Al2_cubic  \n",
       "23        4   LiAl3_cubic  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_phases = [\"Al_fcc\",\"Li_bcc\",\"Li2Al2_cubic\",\"LiAl3_cubic\"]\n",
    "\n",
    "subset_murn = data_murn[data_murn[\"phase\"].isin(list_of_phases)]\n",
    "subset_murn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0d1c799c-f10b-462d-aaea-253cee4b4b3e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LiAl_eam\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:17:48,273 - pyiron_log - WARNING - The job elastic_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:48,454 - pyiron_log - WARNING - The job phonopy_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:48,754 - pyiron_log - WARNING - The job elastic_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:48,922 - pyiron_log - WARNING - The job phonopy_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:49,223 - pyiron_log - WARNING - The job elastic_job_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:49,398 - pyiron_log - WARNING - The job phonopy_job_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:49,706 - pyiron_log - WARNING - The job elastic_job_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:49,875 - pyiron_log - WARNING - The job phonopy_job_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RuNNer-AlLi\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:17:50,177 - pyiron_log - WARNING - The job elastic_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:50,342 - pyiron_log - WARNING - The job phonopy_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:50,639 - pyiron_log - WARNING - The job elastic_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:50,802 - pyiron_log - WARNING - The job phonopy_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:51,099 - pyiron_log - WARNING - The job elastic_job_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:51,271 - pyiron_log - WARNING - The job phonopy_job_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:51,581 - pyiron_log - WARNING - The job elastic_job_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:51,752 - pyiron_log - WARNING - The job phonopy_job_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LiAl_yace\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-08 14:17:52,054 - pyiron_log - WARNING - The job elastic_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:52,218 - pyiron_log - WARNING - The job phonopy_job_Al_fcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:52,515 - pyiron_log - WARNING - The job elastic_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:52,680 - pyiron_log - WARNING - The job phonopy_job_Li_bcc is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:52,983 - pyiron_log - WARNING - The job elastic_job_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:53,159 - pyiron_log - WARNING - The job phonopy_job_Li2Al2_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:53,468 - pyiron_log - WARNING - The job elastic_job_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2022-06-08 14:17:53,638 - pyiron_log - WARNING - The job phonopy_job_LiAl3_cubic is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\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 subset_murn[subset_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",
    "        \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": 18,
   "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 elastic 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": 19,
   "id": "ba95973a-a00f-41a9-b23f-2b4bcf629aaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job table_elastic was saved and received the ID: 1780\n"
     ]
    },
    {
     "data": {
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       "model_id": "89746b00b5e44227af8f4be90b4a7684",
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       "version_minor": 0
      },
      "text/plain": [
       "Loading and filtering jobs:   0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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      },
      "text/plain": [
       "Processing jobs:   0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "    }\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",
       "      <th>phase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1524</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",
       "      <td>Al_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1540</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",
       "      <td>Li_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1556</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>179.464635</td>\n",
       "      <td>54.231219</td>\n",
       "      <td>47.889040</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1573</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>65.443987</td>\n",
       "      <td>47.601166</td>\n",
       "      <td>28.002138</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1590</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>119.613688</td>\n",
       "      <td>59.261331</td>\n",
       "      <td>57.671025</td>\n",
       "      <td>Al</td>\n",
       "      <td>fcc</td>\n",
       "      <td>Al_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1606</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>13.974565</td>\n",
       "      <td>4.476591</td>\n",
       "      <td>13.293350</td>\n",
       "      <td>Li</td>\n",
       "      <td>bcc</td>\n",
       "      <td>Li_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1622</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>124.404880</td>\n",
       "      <td>20.665379</td>\n",
       "      <td>42.673693</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1639</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>88.575923</td>\n",
       "      <td>50.190830</td>\n",
       "      <td>48.202184</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1685</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>133.807535</td>\n",
       "      <td>62.693651</td>\n",
       "      <td>40.423203</td>\n",
       "      <td>Al</td>\n",
       "      <td>fcc</td>\n",
       "      <td>Al_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1701</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>18.307762</td>\n",
       "      <td>13.775557</td>\n",
       "      <td>12.106574</td>\n",
       "      <td>Li</td>\n",
       "      <td>bcc</td>\n",
       "      <td>Li_bcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1717</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>114.275413</td>\n",
       "      <td>13.925574</td>\n",
       "      <td>42.537995</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1734</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>112.037951</td>\n",
       "      <td>42.770574</td>\n",
       "      <td>45.206508</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    job_id    potential         C11        C12        C44 compound  \\\n",
       "0     1524     LiAl_eam  120.339279  66.483631  45.515458       Al   \n",
       "1     1540     LiAl_eam   16.740018  11.018163  12.688217       Li   \n",
       "2     1556     LiAl_eam  179.464635  54.231219  47.889040   Li2Al2   \n",
       "3     1573     LiAl_eam   65.443987  47.601166  28.002138    LiAl3   \n",
       "4     1590  RuNNer-AlLi  119.613688  59.261331  57.671025       Al   \n",
       "5     1606  RuNNer-AlLi   13.974565   4.476591  13.293350       Li   \n",
       "6     1622  RuNNer-AlLi  124.404880  20.665379  42.673693   Li2Al2   \n",
       "7     1639  RuNNer-AlLi   88.575923  50.190830  48.202184    LiAl3   \n",
       "8     1685    LiAl_yace  133.807535  62.693651  40.423203       Al   \n",
       "9     1701    LiAl_yace   18.307762  13.775557  12.106574       Li   \n",
       "10    1717    LiAl_yace  114.275413  13.925574  42.537995   Li2Al2   \n",
       "11    1734    LiAl_yace  112.037951  42.770574  45.206508    LiAl3   \n",
       "\n",
       "   crystal_structure         phase  \n",
       "0                fcc        Al_fcc  \n",
       "1                bcc        Li_bcc  \n",
       "2              cubic  Li2Al2_cubic  \n",
       "3              cubic   LiAl3_cubic  \n",
       "4                fcc        Al_fcc  \n",
       "5                bcc        Li_bcc  \n",
       "6              cubic  Li2Al2_cubic  \n",
       "7              cubic   LiAl3_cubic  \n",
       "8                fcc        Al_fcc  \n",
       "9                bcc        Li_bcc  \n",
       "10             cubic  Li2Al2_cubic  \n",
       "11             cubic   LiAl3_cubic  "
      ]
     },
     "execution_count": 19,
     "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[\"phase\"] = data_elastic.compound + \"_\" + data_elastic.crystal_structure\n",
    "data_elastic = data_elastic[data_elastic[\"phase\"].isin(list_of_phases)]\n",
    "data_elastic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b317b1d3-549b-4e0e-84bf-3cd02a92596d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1872x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax_list = plt.subplots(ncols=len(data_elastic.phase.unique()), nrows=1,)\n",
    "\n",
    "fig.set_figwidth(26)\n",
    "fig.set_figheight(8)\n",
    "\n",
    "color_palette = sns.color_palette(\"tab10\", n_colors=len(data_elastic.potential.unique()))\n",
    "\n",
    "pot = \"LiAl_yace\"\n",
    "\n",
    "\n",
    "for i, phase in enumerate(data_elastic.phase.unique()):\n",
    "    \n",
    "    ax = ax_list[i]\n",
    "    # data = data_elastic[(data_elastic.phase == phase) & (data_elastic[\"potential\"]==\"pot\")]\n",
    "    data = data_elastic[(data_elastic.phase == phase)]\n",
    "    \n",
    "    # DFT data is read from csv files\n",
    "    dft_ref = pd.read_csv(phase.lower()+\"_dos.csv\")\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",
    "        \n",
    "        \n",
    "        ax.plot(phonopy_job[\"output/dos_energies\"], phonopy_job[\"output/dos_total\"], \n",
    "                lw=4,\n",
    "                color=color_palette[j], \n",
    "                label=get_clean_project_name(pot))\n",
    "        \n",
    "       \n",
    "        \n",
    "        ax.set_xlabel(\"Frequency, THz\",fontsize=22)\n",
    "        \n",
    "    ax.plot(dft_ref[\"dos_energy\"],dft_ref[\"dos_total\"],ls=\"--\",lw=3,color=\"k\",label=\"DFT\")\n",
    "        \n",
    "    ax.set_title(f\"{phase}\",fontsize=22)\n",
    "    ax.tick_params(labelsize=16)\n",
    "ax_list[0].set_ylabel(\"DOS\",fontsize=22)\n",
    "\n",
    "ax_list[0].legend(prop={\"size\":16})\n",
    "fig.subplots_adjust(wspace=0.1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b51d876-f408-463b-a337-289830404841",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, ax_list = plt.subplots(ncols=len(data_elastic.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(data_elastic.potential.unique()))\n",
    "\n",
    "\n",
    "# for i, phase in enumerate(data_elastic.phase.unique()):\n",
    "    \n",
    "    \n",
    "#     data = data_elastic[data_elastic.phase == phase]\n",
    "    \n",
    "    \n",
    "    \n",
    "#     for j, pot in enumerate(potentials_list):\n",
    "#         ax = ax_list[j][i]\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(get_clean_project_name(pot)+\"__\"+phase,fontsize=18)\n",
    "#         ax_list[j][0].set_ylabel(\"DOS\")\n",
    "#     # ax_list[0][i].set_title(f\"{phase}\")\n",
    "# fig.subplots_adjust(wspace=0.1, hspace=0.4);"
   ]
  },
  {
   "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 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax_list = plt.subplots(ncols=len(data_elastic.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(data_elastic.potential.unique()))\n",
    "\n",
    "\n",
    "for i, phase in enumerate(data_elastic.phase.unique()):\n",
    "    \n",
    "    ax = ax_list[i]\n",
    "    data = data_elastic[data_elastic.phase == phase]\n",
    "    \n",
    "    n_atom = data_murn[data_murn[\"phase\"]==phase][\"n_atoms\"].iloc[0]\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.cv/n_atom,\n",
    "                lw=4,\n",
    "                label=get_clean_project_name(pot), \n",
    "                color=color_palette[j])\n",
    "        ax.set_xlabel(\"Temperatures, K\",fontsize=18)\n",
    "    ax.set_title(f\"{phase}\",fontsize=22)\n",
    "    ax.tick_params(labelsize=16)\n",
    "ax_list[0].set_ylabel(\"C$_v$\",fontsize=22)\n",
    "\n",
    "ax_list[0].legend(prop={\"size\":16})\n",
    "fig.subplots_adjust(wspace=0.1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7c036a6e-0a66-4adf-8a1e-6ce94ad91ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# phonopy_job.plot_band_structure()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60b72d0f",
   "metadata": {},
   "source": [
    "## (c) Convex hull\n",
    "\n",
    "To assess the stability of the binary phases, we plot a convex hull for the considered phases. \n",
    "\n",
    "For this task we compute the formation energies of the mixed phases relative to ground state energies of equilibrium unary phases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ecb02c3",
   "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>ase_atoms</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>phase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1140</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</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>Al_fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1153</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</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>Al_bcc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   job_id potential                               ase_atoms compound  \\\n",
       "0    1140  LiAl_eam  (Atom('Al', [0.0, 0.0, 0.0], index=0))       Al   \n",
       "1    1153  LiAl_eam  (Atom('Al', [0.0, 0.0, 0.0], index=0))       Al   \n",
       "\n",
       "  crystal_structure         a     eq_vol      eq_bm  eq_energy  n_atoms  \\\n",
       "0               fcc  4.039967  16.495612  85.876912  -3.483097        1   \n",
       "1               bcc  3.898853  16.147864  48.620841  -3.415312        1   \n",
       "\n",
       "    phase  \n",
       "0  Al_fcc  \n",
       "1  Al_bcc  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "# pot = \"LiAl_yace\"\n",
    "\n",
    "# data_convexhull = data_murn[data_murn[\"potential\"]==pot]\n",
    "data_convexhull = data_murn.copy()\n",
    "data_convexhull.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e1b8dd1",
   "metadata": {},
   "source": [
    "Using `Collections.counter` we construct a composition dictionary for all the phases and from that dictionary, we can extract the relative concentrations of Al and Li in each structure\n",
    "\n",
    "Obtain the equilibrium energies for unary Al and Li phases from the Dataframe\n",
    "\n",
    "Calculate the relative formation energies by subtracting the total energies of the mixed phases with the energies of eq Al and Li\n",
    "\n",
    "$$E^{A_xB_y}_{f} = E_{A_xB_y} - (x E_A + yE_B)$$\n",
    "\n",
    "Similarly calculate the formation energies from DFT ref data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b0bba971",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>ase_atoms</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>phase</th>\n",
       "      <th>comp_dict</th>\n",
       "      <th>n_Al</th>\n",
       "      <th>n_Li</th>\n",
       "      <th>cAl</th>\n",
       "      <th>cLi</th>\n",
       "      <th>E_form</th>\n",
       "      <th>E_form_per_atom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1140</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</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>Al_fcc</td>\n",
       "      <td>{'Al': 1}</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1153</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</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>Al_bcc</td>\n",
       "      <td>{'Al': 1}</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.067785</td>\n",
       "      <td>67.785186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1205</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760...</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>5.607502</td>\n",
       "      <td>62.227580</td>\n",
       "      <td>51.472656</td>\n",
       "      <td>-12.774590</td>\n",
       "      <td>4</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "      <td>{'Li': 1, 'Al': 3}</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>-0.567192</td>\n",
       "      <td>-141.797976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1192</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12...</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.165940</td>\n",
       "      <td>58.604895</td>\n",
       "      <td>100.347240</td>\n",
       "      <td>-11.074362</td>\n",
       "      <td>4</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "      <td>{'Li': 2, 'Al': 2}</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>-0.591954</td>\n",
       "      <td>-147.988453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1244</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [2.142967147985671, 1.2372426587287435, 7.662120717536293], index=0), Atom('Li', [-8.783761113500244e-10, 2.4744853189563414, 0.5913679335098909], index=1), Atom('Li', [-8.783761113500...</td>\n",
       "      <td>Li4Al4</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.061226</td>\n",
       "      <td>131.389799</td>\n",
       "      <td>71.221355</td>\n",
       "      <td>-20.506570</td>\n",
       "      <td>8</td>\n",
       "      <td>Li4Al4_cubic</td>\n",
       "      <td>{'Li': 4, 'Al': 4}</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.458247</td>\n",
       "      <td>57.280860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1231</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Al', [2.1548001975659234, 1.244075358781918, 1.861784175000869], index=0), Atom('Al', [-2.154798282819334, 3.732223313213554, 2.6646760238080542], index=1), Atom('Li', [8.560563403365654e-0...</td>\n",
       "      <td>Li3Al2</td>\n",
       "      <td>trigonal</td>\n",
       "      <td>6.094693</td>\n",
       "      <td>72.810229</td>\n",
       "      <td>69.231669</td>\n",
       "      <td>-12.413856</td>\n",
       "      <td>5</td>\n",
       "      <td>Li3Al2_trigonal</td>\n",
       "      <td>{'Al': 2, 'Li': 3}</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-0.173341</td>\n",
       "      <td>-34.668107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1218</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [4.9874611628416465, 1.0099045365192156, 0.8188840806477526], index=0), Atom('Li', [3.1237816780987666, 1.455730745331952, 2.673723152073369], index=1), Atom('Li', [-3.4421956688209843...</td>\n",
       "      <td>Li9Al4</td>\n",
       "      <td>monoclinic</td>\n",
       "      <td>13.023701</td>\n",
       "      <td>190.504374</td>\n",
       "      <td>53.125276</td>\n",
       "      <td>-28.970054</td>\n",
       "      <td>13</td>\n",
       "      <td>Li9Al4_monoclinic</td>\n",
       "      <td>{'Li': 9, 'Al': 4}</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>30.769231</td>\n",
       "      <td>69.230769</td>\n",
       "      <td>0.785300</td>\n",
       "      <td>60.407664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1166</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</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>Li_bcc</td>\n",
       "      <td>{'Li': 1}</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.001096</td>\n",
       "      <td>1.096047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1179</td>\n",
       "      <td>LiAl_eam</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</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>Li_fcc</td>\n",
       "      <td>{'Li': 1}</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1257</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Al</td>\n",
       "      <td>fcc</td>\n",
       "      <td>4.025259</td>\n",
       "      <td>16.355737</td>\n",
       "      <td>76.669339</td>\n",
       "      <td>-3.484016</td>\n",
       "      <td>1</td>\n",
       "      <td>Al_fcc</td>\n",
       "      <td>{'Al': 1}</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1270</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Al</td>\n",
       "      <td>bcc</td>\n",
       "      <td>3.958447</td>\n",
       "      <td>16.870137</td>\n",
       "      <td>51.052272</td>\n",
       "      <td>-3.432183</td>\n",
       "      <td>1</td>\n",
       "      <td>Al_bcc</td>\n",
       "      <td>{'Al': 1}</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051832</td>\n",
       "      <td>51.832389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1322</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998...</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>5.700455</td>\n",
       "      <td>65.403086</td>\n",
       "      <td>59.308440</td>\n",
       "      <td>-12.574696</td>\n",
       "      <td>4</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "      <td>{'Li': 1, 'Al': 3}</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>-0.353389</td>\n",
       "      <td>-88.347230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1309</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12...</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.376805</td>\n",
       "      <td>64.816143</td>\n",
       "      <td>57.934650</td>\n",
       "      <td>-11.212634</td>\n",
       "      <td>4</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "      <td>{'Li': 2, 'Al': 2}</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>-0.706083</td>\n",
       "      <td>-176.520795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1361</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [2.220260976080854, 1.2818682724036983, 7.872085429446316], index=0), Atom('Li', [1.722758777253687e-10, 2.5637365444716322, 0.6790950189344616], index=1), Atom('Li', [1.72275877725368...</td>\n",
       "      <td>Li4Al4</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.279846</td>\n",
       "      <td>146.014891</td>\n",
       "      <td>37.664442</td>\n",
       "      <td>-21.680919</td>\n",
       "      <td>8</td>\n",
       "      <td>Li4Al4_cubic</td>\n",
       "      <td>{'Li': 4, 'Al': 4}</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>-0.667816</td>\n",
       "      <td>-83.477017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1348</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Al', [2.2338755345732753, 1.289729472183878, 1.9126243306628208], index=0), Atom('Al', [-2.233873547699001, 3.869185551846968, 2.7799443936883206], index=1), Atom('Li', [9.007133262260959e-...</td>\n",
       "      <td>Li3Al2</td>\n",
       "      <td>trigonal</td>\n",
       "      <td>6.318351</td>\n",
       "      <td>81.143544</td>\n",
       "      <td>44.574696</td>\n",
       "      <td>-13.185198</td>\n",
       "      <td>5</td>\n",
       "      <td>Li3Al2_trigonal</td>\n",
       "      <td>{'Al': 2, 'Li': 3}</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-0.909387</td>\n",
       "      <td>-181.877324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1335</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [5.206051477294367, 1.0619663179427192, 0.8311820920214751], index=0), Atom('Li', [3.28638171437237, 1.5211864250363467, 2.7226207058417775], index=1), Atom('Li', [-3.6198784902055765,...</td>\n",
       "      <td>Li9Al4</td>\n",
       "      <td>monoclinic</td>\n",
       "      <td>13.640614</td>\n",
       "      <td>218.932018</td>\n",
       "      <td>33.874957</td>\n",
       "      <td>-31.820765</td>\n",
       "      <td>13</td>\n",
       "      <td>Li9Al4_monoclinic</td>\n",
       "      <td>{'Li': 9, 'Al': 4}</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>30.769231</td>\n",
       "      <td>69.230769</td>\n",
       "      <td>-1.961363</td>\n",
       "      <td>-150.874092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1283</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Li</td>\n",
       "      <td>bcc</td>\n",
       "      <td>4.211118</td>\n",
       "      <td>20.286595</td>\n",
       "      <td>8.517306</td>\n",
       "      <td>-1.755918</td>\n",
       "      <td>1</td>\n",
       "      <td>Li_bcc</td>\n",
       "      <td>{'Li': 1}</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.013342</td>\n",
       "      <td>13.341610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1296</td>\n",
       "      <td>RuNNer-AlLi</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Li</td>\n",
       "      <td>fcc</td>\n",
       "      <td>3.967043</td>\n",
       "      <td>15.678901</td>\n",
       "      <td>147.215464</td>\n",
       "      <td>-1.769260</td>\n",
       "      <td>1</td>\n",
       "      <td>Li_fcc</td>\n",
       "      <td>{'Li': 1}</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1393</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Al</td>\n",
       "      <td>fcc</td>\n",
       "      <td>4.044553</td>\n",
       "      <td>16.541594</td>\n",
       "      <td>87.130427</td>\n",
       "      <td>-3.478909</td>\n",
       "      <td>1</td>\n",
       "      <td>Al_fcc</td>\n",
       "      <td>{'Al': 1}</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1406</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Al</td>\n",
       "      <td>bcc</td>\n",
       "      <td>3.953036</td>\n",
       "      <td>16.811334</td>\n",
       "      <td>72.667242</td>\n",
       "      <td>-3.388831</td>\n",
       "      <td>1</td>\n",
       "      <td>Al_bcc</td>\n",
       "      <td>{'Al': 1}</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090078</td>\n",
       "      <td>90.077889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1464</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993...</td>\n",
       "      <td>LiAl3</td>\n",
       "      <td>cubic</td>\n",
       "      <td>5.686989</td>\n",
       "      <td>65.028366</td>\n",
       "      <td>66.254925</td>\n",
       "      <td>-12.569153</td>\n",
       "      <td>4</td>\n",
       "      <td>LiAl3_cubic</td>\n",
       "      <td>{'Li': 1, 'Al': 3}</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>-0.376321</td>\n",
       "      <td>-94.080320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1451</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e...</td>\n",
       "      <td>Li2Al2</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.367064</td>\n",
       "      <td>64.521799</td>\n",
       "      <td>46.107162</td>\n",
       "      <td>-11.185880</td>\n",
       "      <td>4</td>\n",
       "      <td>Li2Al2_cubic</td>\n",
       "      <td>{'Li': 2, 'Al': 2}</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>-0.715855</td>\n",
       "      <td>-178.963696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1506</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [2.2269869888586107, 1.285751535686306, 7.864026721150146], index=0), Atom('Li', [-1.5554058443124377e-09, 2.571503074062492, 0.7130584901440213], index=1), Atom('Li', [-1.555405844312...</td>\n",
       "      <td>Li4Al4</td>\n",
       "      <td>cubic</td>\n",
       "      <td>6.298870</td>\n",
       "      <td>147.356944</td>\n",
       "      <td>46.701117</td>\n",
       "      <td>-21.607231</td>\n",
       "      <td>8</td>\n",
       "      <td>Li4Al4_cubic</td>\n",
       "      <td>{'Li': 4, 'Al': 4}</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>-0.667180</td>\n",
       "      <td>-83.397512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1493</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Al', [2.2270976540671734, 1.2858164055924044, 1.9025646270076813], index=0), Atom('Al', [-2.227095628822777, 3.8574462424884515, 2.7757665665986657], index=1), Atom('Li', [8.407589514518869...</td>\n",
       "      <td>Li3Al2</td>\n",
       "      <td>trigonal</td>\n",
       "      <td>6.299181</td>\n",
       "      <td>80.375104</td>\n",
       "      <td>39.643133</td>\n",
       "      <td>-13.138303</td>\n",
       "      <td>5</td>\n",
       "      <td>Li3Al2_trigonal</td>\n",
       "      <td>{'Al': 2, 'Li': 3}</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-0.912174</td>\n",
       "      <td>-182.434806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1480</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [5.141009159558869, 1.0571139195527752, 0.820249453790277], index=0), Atom('Li', [3.2705789348169056, 1.5045550288016276, 2.715159327393234], index=1), Atom('Li', [-3.601125467999465, ...</td>\n",
       "      <td>Li9Al4</td>\n",
       "      <td>monoclinic</td>\n",
       "      <td>13.519944</td>\n",
       "      <td>213.136118</td>\n",
       "      <td>33.963240</td>\n",
       "      <td>-31.796316</td>\n",
       "      <td>13</td>\n",
       "      <td>Li9Al4_monoclinic</td>\n",
       "      <td>{'Li': 9, 'Al': 4}</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>30.769231</td>\n",
       "      <td>69.230769</td>\n",
       "      <td>-2.075747</td>\n",
       "      <td>-159.672835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1419</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Li</td>\n",
       "      <td>bcc</td>\n",
       "      <td>4.216389</td>\n",
       "      <td>20.403222</td>\n",
       "      <td>15.823747</td>\n",
       "      <td>-1.756104</td>\n",
       "      <td>1</td>\n",
       "      <td>Li_bcc</td>\n",
       "      <td>{'Li': 1}</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1435</td>\n",
       "      <td>LiAl_yace</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0))</td>\n",
       "      <td>Li</td>\n",
       "      <td>fcc</td>\n",
       "      <td>4.331457</td>\n",
       "      <td>20.318983</td>\n",
       "      <td>14.231625</td>\n",
       "      <td>-1.755594</td>\n",
       "      <td>1</td>\n",
       "      <td>Li_fcc</td>\n",
       "      <td>{'Li': 1}</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.509341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    job_id    potential  \\\n",
       "0     1140     LiAl_eam   \n",
       "1     1153     LiAl_eam   \n",
       "5     1205     LiAl_eam   \n",
       "4     1192     LiAl_eam   \n",
       "8     1244     LiAl_eam   \n",
       "7     1231     LiAl_eam   \n",
       "6     1218     LiAl_eam   \n",
       "2     1166     LiAl_eam   \n",
       "3     1179     LiAl_eam   \n",
       "9     1257  RuNNer-AlLi   \n",
       "10    1270  RuNNer-AlLi   \n",
       "14    1322  RuNNer-AlLi   \n",
       "13    1309  RuNNer-AlLi   \n",
       "17    1361  RuNNer-AlLi   \n",
       "16    1348  RuNNer-AlLi   \n",
       "15    1335  RuNNer-AlLi   \n",
       "11    1283  RuNNer-AlLi   \n",
       "12    1296  RuNNer-AlLi   \n",
       "18    1393    LiAl_yace   \n",
       "19    1406    LiAl_yace   \n",
       "23    1464    LiAl_yace   \n",
       "22    1451    LiAl_yace   \n",
       "26    1506    LiAl_yace   \n",
       "25    1493    LiAl_yace   \n",
       "24    1480    LiAl_yace   \n",
       "20    1419    LiAl_yace   \n",
       "21    1435    LiAl_yace   \n",
       "\n",
       "                                                                                                                                                                                                  ase_atoms  \\\n",
       "0                                                                                                                                                                    (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "1                                                                                                                                                                    (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "5   (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [1.9825515172760235, 1.9825515172760237, 2.427925369776811e-16], index=1), Atom('Al', [1.9825515172760235, 1.2139626848884054e-16, 1.9825515172760...   \n",
       "4   (Atom('Li', [4.359978178265943, 2.5172345748814795, 1.7799536377360747], index=0), Atom('Li', [6.53996726740165, 3.775851862320358, 2.669930456604317], index=1), Atom('Al', [-3.964456982410852e-12...   \n",
       "8   (Atom('Li', [2.142967147985671, 1.2372426587287435, 7.662120717536293], index=0), Atom('Li', [-8.783761113500244e-10, 2.4744853189563414, 0.5913679335098909], index=1), Atom('Li', [-8.783761113500...   \n",
       "7   (Atom('Al', [2.1548001975659234, 1.244075358781918, 1.861784175000869], index=0), Atom('Al', [-2.154798282819334, 3.732223313213554, 2.6646760238080542], index=1), Atom('Li', [8.560563403365654e-0...   \n",
       "6   (Atom('Li', [4.9874611628416465, 1.0099045365192156, 0.8188840806477526], index=0), Atom('Li', [3.1237816780987666, 1.455730745331952, 2.673723152073369], index=1), Atom('Li', [-3.4421956688209843...   \n",
       "2                                                                                                                                                                    (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "3                                                                                                                                                                    (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "9                                                                                                                                                                    (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "10                                                                                                                                                                   (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "14  (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0154153406879987, 2.0154153406879987, 2.46817194592603e-16], index=1), Atom('Al', [2.0154153406879987, 1.234085972963015e-16, 2.015415340687998...   \n",
       "13  (Atom('Li', [4.509081801264686, 2.603319591757272, 1.8408249369278522], index=0), Atom('Li', [6.763622701898693, 3.90497938763465, 2.7612374053913604], index=1), Atom('Al', [-3.844724064520768e-12...   \n",
       "17  (Atom('Li', [2.220260976080854, 1.2818682724036983, 7.872085429446316], index=0), Atom('Li', [1.722758777253687e-10, 2.5637365444716322, 0.6790950189344616], index=1), Atom('Li', [1.72275877725368...   \n",
       "16  (Atom('Al', [2.2338755345732753, 1.289729472183878, 1.9126243306628208], index=0), Atom('Al', [-2.233873547699001, 3.869185551846968, 2.7799443936883206], index=1), Atom('Li', [9.007133262260959e-...   \n",
       "15  (Atom('Li', [5.206051477294367, 1.0619663179427192, 0.8311820920214751], index=0), Atom('Li', [3.28638171437237, 1.5211864250363467, 2.7226207058417775], index=1), Atom('Li', [-3.6198784902055765,...   \n",
       "11                                                                                                                                                                   (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "12                                                                                                                                                                   (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "18                                                                                                                                                                   (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "19                                                                                                                                                                   (Atom('Al', [0.0, 0.0, 0.0], index=0))   \n",
       "23  (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0106543994993293, 2.0106543994993293, 2.462341474538397e-16], index=1), Atom('Al', [2.0106543994993293, 1.2311707372691985e-16, 2.0106543994993...   \n",
       "22  (Atom('Li', [4.5021943685456485, 2.599343130623782, 1.8380131542949232], index=0), Atom('Li', [6.753291552821257, 3.8990146959337566, 2.7570197314419675], index=1), Atom('Al', [-3.838851410290508e...   \n",
       "26  (Atom('Li', [2.2269869888586107, 1.285751535686306, 7.864026721150146], index=0), Atom('Li', [-1.5554058443124377e-09, 2.571503074062492, 0.7130584901440213], index=1), Atom('Li', [-1.555405844312...   \n",
       "25  (Atom('Al', [2.2270976540671734, 1.2858164055924044, 1.9025646270076813], index=0), Atom('Al', [-2.227095628822777, 3.8574462424884515, 2.7757665665986657], index=1), Atom('Li', [8.407589514518869...   \n",
       "24  (Atom('Li', [5.141009159558869, 1.0571139195527752, 0.820249453790277], index=0), Atom('Li', [3.2705789348169056, 1.5045550288016276, 2.715159327393234], index=1), Atom('Li', [-3.601125467999465, ...   \n",
       "20                                                                                                                                                                   (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "21                                                                                                                                                                   (Atom('Li', [0.0, 0.0, 0.0], index=0))   \n",
       "\n",
       "   compound crystal_structure          a      eq_vol       eq_bm  eq_energy  \\\n",
       "0        Al               fcc   4.039967   16.495612   85.876912  -3.483097   \n",
       "1        Al               bcc   3.898853   16.147864   48.620841  -3.415312   \n",
       "5     LiAl3             cubic   5.607502   62.227580   51.472656 -12.774590   \n",
       "4    Li2Al2             cubic   6.165940   58.604895  100.347240 -11.074362   \n",
       "8    Li4Al4             cubic   6.061226  131.389799   71.221355 -20.506570   \n",
       "7    Li3Al2          trigonal   6.094693   72.810229   69.231669 -12.413856   \n",
       "6    Li9Al4        monoclinic  13.023701  190.504374   53.125276 -28.970054   \n",
       "2        Li               bcc   4.195477   20.114514   13.690609  -1.757011   \n",
       "3        Li               fcc   4.253841   19.241330   13.985972  -1.758107   \n",
       "9        Al               fcc   4.025259   16.355737   76.669339  -3.484016   \n",
       "10       Al               bcc   3.958447   16.870137   51.052272  -3.432183   \n",
       "14    LiAl3             cubic   5.700455   65.403086   59.308440 -12.574696   \n",
       "13   Li2Al2             cubic   6.376805   64.816143   57.934650 -11.212634   \n",
       "17   Li4Al4             cubic   6.279846  146.014891   37.664442 -21.680919   \n",
       "16   Li3Al2          trigonal   6.318351   81.143544   44.574696 -13.185198   \n",
       "15   Li9Al4        monoclinic  13.640614  218.932018   33.874957 -31.820765   \n",
       "11       Li               bcc   4.211118   20.286595    8.517306  -1.755918   \n",
       "12       Li               fcc   3.967043   15.678901  147.215464  -1.769260   \n",
       "18       Al               fcc   4.044553   16.541594   87.130427  -3.478909   \n",
       "19       Al               bcc   3.953036   16.811334   72.667242  -3.388831   \n",
       "23    LiAl3             cubic   5.686989   65.028366   66.254925 -12.569153   \n",
       "22   Li2Al2             cubic   6.367064   64.521799   46.107162 -11.185880   \n",
       "26   Li4Al4             cubic   6.298870  147.356944   46.701117 -21.607231   \n",
       "25   Li3Al2          trigonal   6.299181   80.375104   39.643133 -13.138303   \n",
       "24   Li9Al4        monoclinic  13.519944  213.136118   33.963240 -31.796316   \n",
       "20       Li               bcc   4.216389   20.403222   15.823747  -1.756104   \n",
       "21       Li               fcc   4.331457   20.318983   14.231625  -1.755594   \n",
       "\n",
       "    n_atoms              phase           comp_dict  n_Al  n_Li         cAl  \\\n",
       "0         1             Al_fcc           {'Al': 1}     1     0  100.000000   \n",
       "1         1             Al_bcc           {'Al': 1}     1     0  100.000000   \n",
       "5         4        LiAl3_cubic  {'Li': 1, 'Al': 3}     3     1   75.000000   \n",
       "4         4       Li2Al2_cubic  {'Li': 2, 'Al': 2}     2     2   50.000000   \n",
       "8         8       Li4Al4_cubic  {'Li': 4, 'Al': 4}     4     4   50.000000   \n",
       "7         5    Li3Al2_trigonal  {'Al': 2, 'Li': 3}     2     3   40.000000   \n",
       "6        13  Li9Al4_monoclinic  {'Li': 9, 'Al': 4}     4     9   30.769231   \n",
       "2         1             Li_bcc           {'Li': 1}     0     1    0.000000   \n",
       "3         1             Li_fcc           {'Li': 1}     0     1    0.000000   \n",
       "9         1             Al_fcc           {'Al': 1}     1     0  100.000000   \n",
       "10        1             Al_bcc           {'Al': 1}     1     0  100.000000   \n",
       "14        4        LiAl3_cubic  {'Li': 1, 'Al': 3}     3     1   75.000000   \n",
       "13        4       Li2Al2_cubic  {'Li': 2, 'Al': 2}     2     2   50.000000   \n",
       "17        8       Li4Al4_cubic  {'Li': 4, 'Al': 4}     4     4   50.000000   \n",
       "16        5    Li3Al2_trigonal  {'Al': 2, 'Li': 3}     2     3   40.000000   \n",
       "15       13  Li9Al4_monoclinic  {'Li': 9, 'Al': 4}     4     9   30.769231   \n",
       "11        1             Li_bcc           {'Li': 1}     0     1    0.000000   \n",
       "12        1             Li_fcc           {'Li': 1}     0     1    0.000000   \n",
       "18        1             Al_fcc           {'Al': 1}     1     0  100.000000   \n",
       "19        1             Al_bcc           {'Al': 1}     1     0  100.000000   \n",
       "23        4        LiAl3_cubic  {'Li': 1, 'Al': 3}     3     1   75.000000   \n",
       "22        4       Li2Al2_cubic  {'Li': 2, 'Al': 2}     2     2   50.000000   \n",
       "26        8       Li4Al4_cubic  {'Li': 4, 'Al': 4}     4     4   50.000000   \n",
       "25        5    Li3Al2_trigonal  {'Al': 2, 'Li': 3}     2     3   40.000000   \n",
       "24       13  Li9Al4_monoclinic  {'Li': 9, 'Al': 4}     4     9   30.769231   \n",
       "20        1             Li_bcc           {'Li': 1}     0     1    0.000000   \n",
       "21        1             Li_fcc           {'Li': 1}     0     1    0.000000   \n",
       "\n",
       "           cLi    E_form  E_form_per_atom  \n",
       "0     0.000000  0.000000         0.000000  \n",
       "1     0.000000  0.067785        67.785186  \n",
       "5    25.000000 -0.567192      -141.797976  \n",
       "4    50.000000 -0.591954      -147.988453  \n",
       "8    50.000000  0.458247        57.280860  \n",
       "7    60.000000 -0.173341       -34.668107  \n",
       "6    69.230769  0.785300        60.407664  \n",
       "2   100.000000  0.001096         1.096047  \n",
       "3   100.000000  0.000000         0.000000  \n",
       "9     0.000000  0.000000         0.000000  \n",
       "10    0.000000  0.051832        51.832389  \n",
       "14   25.000000 -0.353389       -88.347230  \n",
       "13   50.000000 -0.706083      -176.520795  \n",
       "17   50.000000 -0.667816       -83.477017  \n",
       "16   60.000000 -0.909387      -181.877324  \n",
       "15   69.230769 -1.961363      -150.874092  \n",
       "11  100.000000  0.013342        13.341610  \n",
       "12  100.000000  0.000000         0.000000  \n",
       "18    0.000000  0.000000         0.000000  \n",
       "19    0.000000  0.090078        90.077889  \n",
       "23   25.000000 -0.376321       -94.080320  \n",
       "22   50.000000 -0.715855      -178.963696  \n",
       "26   50.000000 -0.667180       -83.397512  \n",
       "25   60.000000 -0.912174      -182.434806  \n",
       "24   69.230769 -2.075747      -159.672835  \n",
       "20  100.000000  0.000000         0.000000  \n",
       "21  100.000000  0.000509         0.509341  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_e_form(data_convexhull):\n",
    "    data_convexhull[\"comp_dict\"] = data_convexhull[\"ase_atoms\"].map(lambda at: Counter(at.get_chemical_symbols()))\n",
    "    data_convexhull[\"n_Al\"] = data_convexhull[\"comp_dict\"].map(lambda d: d.get(\"Al\",0))\n",
    "    data_convexhull[\"n_Li\"] = data_convexhull[\"comp_dict\"].map(lambda d: d.get(\"Li\",0))\n",
    "\n",
    "    data_convexhull[\"cAl\"]= data_convexhull[\"n_Al\"]/data_convexhull[\"n_atoms\"] * 100\n",
    "    data_convexhull[\"cLi\"]= data_convexhull[\"n_Li\"]/data_convexhull[\"n_atoms\"] * 100\n",
    "\n",
    "    E_f_Al = data_convexhull.loc[data_convexhull[\"n_Li\"]==0,\"eq_energy\"].min()\n",
    "    E_f_Li = data_convexhull.loc[data_convexhull[\"n_Al\"]==0,\"eq_energy\"].min()\n",
    "\n",
    "    data_convexhull[\"E_form\"]=(data_convexhull[\"eq_energy\"])-(data_convexhull[[\"n_Al\",\"n_Li\"]].values * [E_f_Al, E_f_Li]).sum(axis=1)\n",
    "    data_convexhull[\"E_form_per_atom\"] = data_convexhull[\"E_form\"]/data_convexhull[\"n_atoms\"] * 1e3\n",
    "\n",
    "    data_convexhull = data_convexhull.sort_values(\"cLi\")\n",
    "\n",
    "    return data_convexhull\n",
    "\n",
    "df_eam = get_e_form(data_murn[data_murn[\"potential\"]==\"LiAl_eam\"].copy())\n",
    "df_nnp = get_e_form(data_murn[data_murn[\"potential\"]==\"RuNNer-AlLi\"].copy())\n",
    "df_ace = get_e_form(data_murn[data_murn[\"potential\"]==\"LiAl_yace\"].copy())\n",
    "\n",
    "data_convexhull = pd.concat([df_eam,df_nnp,df_ace])\n",
    "data_convexhull"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48cfb589-9b0e-4bd4-aa87-20d0c848c80c",
   "metadata": {},
   "source": [
    "Read df which contains DFT ref data for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "35df2f07",
   "metadata": {
    "tags": []
   },
   "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>energy</th>\n",
       "      <th>vol</th>\n",
       "      <th>compound</th>\n",
       "      <th>ao</th>\n",
       "      <th>number_of_atoms</th>\n",
       "      <th>comp_dict</th>\n",
       "      <th>n_Al</th>\n",
       "      <th>n_Li</th>\n",
       "      <th>cAl</th>\n",
       "      <th>cLi</th>\n",
       "      <th>E_form</th>\n",
       "      <th>E_form_per_atom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>/home/users/lysogy36/tools/VASP/Al-Li/DFT/Al_fcc/murn/strain_1_0/data.json</td>\n",
       "      <td>-13.930995</td>\n",
       "      <td>16.484415</td>\n",
       "      <td>Al_fcc</td>\n",
       "      <td>(Atom('Al', [0.0, 0.0, 0.0], index=0), Atom('Al', [0.0, 2.019983601551115, 2.019983601551115], index=1), Atom('Al', [2.019983601551115, 0.0, 2.019983601551115], index=2), Atom('Al', [2.01998360155...</td>\n",
       "      <td>4</td>\n",
       "      <td>{'Al': 4}</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>/home/users/lysogy36/tools/VASP/Al-Li/DFT/LiAl3_mp-10890/murn/strain_1_0/data.json</td>\n",
       "      <td>-12.597018</td>\n",
       "      <td>16.295840</td>\n",
       "      <td>LiAl3_mp-10890</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0122514573524146, 2.0122514573524146, 0.0], index=1), Atom('Al', [2.0122514573524146, 0.0, 2.0122514573524146], index=2), Atom('Al', [0.0, 2.01...</td>\n",
       "      <td>4</td>\n",
       "      <td>{'Li': 1, 'Al': 3}</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>-0.392474</td>\n",
       "      <td>-98.118408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>/home/users/lysogy36/tools/VASP/Al-Li/DFT/LiAl_mp-1067/murn/strain_1_0/data.json</td>\n",
       "      <td>-11.204795</td>\n",
       "      <td>16.028228</td>\n",
       "      <td>LiAl_mp-1067</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Li', [2.246243529971499, 1.2968693066945, 0.9170250810763773], index=1), Atom('Al', [4.492487059942998, 2.593738613389, 1.8340501621527545], index=2), ...</td>\n",
       "      <td>4</td>\n",
       "      <td>{'Li': 2, 'Al': 2}</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>-0.726701</td>\n",
       "      <td>-181.675339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>/home/users/lysogy36/tools/VASP/Al-Li/DFT/LiAl_mp-1079240/murn/strain_1_0/data.json</td>\n",
       "      <td>-21.715330</td>\n",
       "      <td>18.537039</td>\n",
       "      <td>LiAl_mp-1079240</td>\n",
       "      <td>(Atom('Li', [-2.093764484173552e-06, 2.574581270471953, 3.588630766943668], index=0), Atom('Li', [2.229653899294873, 1.2872887040708958, 5.022609593138096], index=1), Atom('Li', [2.229653899294873...</td>\n",
       "      <td>8</td>\n",
       "      <td>{'Li': 4, 'Al': 4}</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>-0.759143</td>\n",
       "      <td>-94.892853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>/home/users/lysogy36/tools/VASP/Al-Li/DFT/Li3Al2_mp-16506/murn/strain_1_0/data.json</td>\n",
       "      <td>-13.176984</td>\n",
       "      <td>16.098544</td>\n",
       "      <td>Li3Al2_mp-16506</td>\n",
       "      <td>(Atom('Li', [7.387307289355338, 3.3557842846492325, 2.205190367378745], index=0), Atom('Li', [4.984874333407062, 2.2644466100798333, 1.488038392346624], index=1), Atom('Li', [0.0, 0.0, 0.0], index...</td>\n",
       "      <td>5</td>\n",
       "      <td>{'Li': 3, 'Al': 2}</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-0.942593</td>\n",
       "      <td>-188.518538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>/home/users/lysogy36/tools/VASP/Al-Li/DFT/Li9Al4_mp-568404/murn/strain_1_0/data.json</td>\n",
       "      <td>-31.786765</td>\n",
       "      <td>16.532577</td>\n",
       "      <td>Li9Al4_mp-568404</td>\n",
       "      <td>(Atom('Li', [15.085585487572331, 3.6087478779487228, 4.372653838370371], index=0), Atom('Li', [13.209884188064274, 3.160045831227256, 2.4668794892606694], index=1), Atom('Li', [6.31626414433567, 1...</td>\n",
       "      <td>13</td>\n",
       "      <td>{'Li': 9, 'Al': 4}</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>30.769231</td>\n",
       "      <td>69.230769</td>\n",
       "      <td>-2.049089</td>\n",
       "      <td>-157.622253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>/home/users/lysogy36/tools/VASP/Al-Li/DFT/Li_bcc/murn/strain_1_0/data.json</td>\n",
       "      <td>-3.512596</td>\n",
       "      <td>20.099126</td>\n",
       "      <td>Li_bcc</td>\n",
       "      <td>(Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Li', [1.712796338409787, 1.712796338409787, 1.712796338409787], index=1))</td>\n",
       "      <td>2</td>\n",
       "      <td>{'Li': 2}</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                      name  \\\n",
       "438             /home/users/lysogy36/tools/VASP/Al-Li/DFT/Al_fcc/murn/strain_1_0/data.json   \n",
       "910     /home/users/lysogy36/tools/VASP/Al-Li/DFT/LiAl3_mp-10890/murn/strain_1_0/data.json   \n",
       "1950      /home/users/lysogy36/tools/VASP/Al-Li/DFT/LiAl_mp-1067/murn/strain_1_0/data.json   \n",
       "1275   /home/users/lysogy36/tools/VASP/Al-Li/DFT/LiAl_mp-1079240/murn/strain_1_0/data.json   \n",
       "652    /home/users/lysogy36/tools/VASP/Al-Li/DFT/Li3Al2_mp-16506/murn/strain_1_0/data.json   \n",
       "231   /home/users/lysogy36/tools/VASP/Al-Li/DFT/Li9Al4_mp-568404/murn/strain_1_0/data.json   \n",
       "1343            /home/users/lysogy36/tools/VASP/Al-Li/DFT/Li_bcc/murn/strain_1_0/data.json   \n",
       "\n",
       "         energy        vol          compound  \\\n",
       "438  -13.930995  16.484415            Al_fcc   \n",
       "910  -12.597018  16.295840    LiAl3_mp-10890   \n",
       "1950 -11.204795  16.028228      LiAl_mp-1067   \n",
       "1275 -21.715330  18.537039   LiAl_mp-1079240   \n",
       "652  -13.176984  16.098544   Li3Al2_mp-16506   \n",
       "231  -31.786765  16.532577  Li9Al4_mp-568404   \n",
       "1343  -3.512596  20.099126            Li_bcc   \n",
       "\n",
       "                                                                                                                                                                                                           ao  \\\n",
       "438   (Atom('Al', [0.0, 0.0, 0.0], index=0), Atom('Al', [0.0, 2.019983601551115, 2.019983601551115], index=1), Atom('Al', [2.019983601551115, 0.0, 2.019983601551115], index=2), Atom('Al', [2.01998360155...   \n",
       "910   (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Al', [2.0122514573524146, 2.0122514573524146, 0.0], index=1), Atom('Al', [2.0122514573524146, 0.0, 2.0122514573524146], index=2), Atom('Al', [0.0, 2.01...   \n",
       "1950  (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Li', [2.246243529971499, 1.2968693066945, 0.9170250810763773], index=1), Atom('Al', [4.492487059942998, 2.593738613389, 1.8340501621527545], index=2), ...   \n",
       "1275  (Atom('Li', [-2.093764484173552e-06, 2.574581270471953, 3.588630766943668], index=0), Atom('Li', [2.229653899294873, 1.2872887040708958, 5.022609593138096], index=1), Atom('Li', [2.229653899294873...   \n",
       "652   (Atom('Li', [7.387307289355338, 3.3557842846492325, 2.205190367378745], index=0), Atom('Li', [4.984874333407062, 2.2644466100798333, 1.488038392346624], index=1), Atom('Li', [0.0, 0.0, 0.0], index...   \n",
       "231   (Atom('Li', [15.085585487572331, 3.6087478779487228, 4.372653838370371], index=0), Atom('Li', [13.209884188064274, 3.160045831227256, 2.4668794892606694], index=1), Atom('Li', [6.31626414433567, 1...   \n",
       "1343                                                                                   (Atom('Li', [0.0, 0.0, 0.0], index=0), Atom('Li', [1.712796338409787, 1.712796338409787, 1.712796338409787], index=1))   \n",
       "\n",
       "      number_of_atoms           comp_dict  n_Al  n_Li         cAl         cLi  \\\n",
       "438                 4           {'Al': 4}     4     0  100.000000    0.000000   \n",
       "910                 4  {'Li': 1, 'Al': 3}     3     1   75.000000   25.000000   \n",
       "1950                4  {'Li': 2, 'Al': 2}     2     2   50.000000   50.000000   \n",
       "1275                8  {'Li': 4, 'Al': 4}     4     4   50.000000   50.000000   \n",
       "652                 5  {'Li': 3, 'Al': 2}     2     3   40.000000   60.000000   \n",
       "231                13  {'Li': 9, 'Al': 4}     4     9   30.769231   69.230769   \n",
       "1343                2           {'Li': 2}     0     2    0.000000  100.000000   \n",
       "\n",
       "        E_form  E_form_per_atom  \n",
       "438   0.000000         0.000000  \n",
       "910  -0.392474       -98.118408  \n",
       "1950 -0.726701      -181.675339  \n",
       "1275 -0.759143       -94.892853  \n",
       "652  -0.942593      -188.518538  \n",
       "231  -2.049089      -157.622253  \n",
       "1343  0.000000         0.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convex_ref = pd.read_pickle(\"dft_convexhull_ref.pckl\")\n",
    "convex_ref"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f0c166-ad9c-48df-ad42-93319a7ca854",
   "metadata": {},
   "source": [
    "Define a function to automatically get the mathematical convex hull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1fb8d855-98b5-46d4-9e62-f0abd3099fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial import ConvexHull,convex_hull_plot_2d\n",
    "\n",
    "def get_convexhull(df):\n",
    "    df_tmp = df.reset_index()\n",
    "\n",
    "    points = np.zeros([len(df_tmp[\"cLi\"]),2])\n",
    "\n",
    "    for i,row in df_tmp.iterrows():\n",
    "        points[i,0], points[i,1] =  float(row[\"cLi\"]), float(row[\"E_form_per_atom\"])\n",
    "\n",
    "    hull = ConvexHull(points)\n",
    "    return hull,points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ea09f703-8f80-41be-972d-1d894885c2ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1584x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(22,8),ncols=len(potentials_list),constrained_layout=True,sharey=\"row\")\n",
    "\n",
    "dfs = ([pd.DataFrame(y) for x, y in data_convexhull.groupby(by='potential', as_index=False)])\n",
    "\n",
    "for i,pot in enumerate(potentials_list):\n",
    "    \n",
    "    df_tmp = dfs[i].copy()\n",
    "    \n",
    "    \n",
    "    \n",
    "    ax[i].scatter(df_tmp[\"cLi\"],df_tmp[\"E_form_per_atom\"],marker=\"o\",s=50)\n",
    "    \n",
    "    \n",
    "    df_tmp = df_tmp[(df_tmp[\"E_form_per_atom\"]<0.1) & (df_tmp[\"E_form\"]<0.1)]\n",
    "    hull,points = get_convexhull(df_tmp)\n",
    "    \n",
    "    for simplex in hull.simplices:\n",
    "        ax[i].plot(points[simplex, 0], points[simplex, 1], 'k-')\n",
    "    \n",
    "    \n",
    "    ax[i].axhline(0,ls=\"--\",color=\"k\")\n",
    "    ax[i].plot(df_tmp[\"cLi\"], df_tmp[\"E_form_per_atom\"],\"o\",markersize=10,label=\"potential\")\n",
    "    ax[i].scatter(convex_ref[\"cLi\"],convex_ref[\"E_form_per_atom\"],marker=\"x\",s=70,\n",
    "                  label=\"DFT\")\n",
    "    ax[i].legend(prop={\"size\":16})\n",
    "    ax[i].set_xlabel(\"Li,%\",fontsize=\"20\")\n",
    "    ax[i].set_ylabel(\"E$_f$, meV/atom\",fontsize=\"20\")\n",
    "    ax[i].tick_params(labelsize=20,axis=\"both\")\n",
    "# ax.set_ylim(-200,10)\n",
    "    for _,row in dfs[i].iterrows():\n",
    "        ax[i].text((row[\"cLi\"]+0.01),row[\"E_form_per_atom\"],row[\"phase\"],size=12)\n",
    "        \n",
    "    ax[i].set_title(dfs[i].iloc[0][\"potential\"],fontsize=22)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ca9dbcc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total run time for the notebook 180.9642095565796 seconds\n"
     ]
    }
   ],
   "source": [
    "time_stop = time.time()\n",
    "print(f\"Total run time for the notebook {time_stop - time_start} seconds\")"
   ]
  }
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