examples/Plotting and Analyzing a Phase Diagram using the Materials API.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymatgen.ext.matproj import MPRester\n",
    "from pymatgen.analysis.phase_diagram import PhaseDiagram, PDPlotter\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the phase diagram\n",
    "\n",
    "To generate a phase diagram, we obtain entries from the Materials Project and call the PhaseDiagram class in pymatgen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This initializes the REST adaptor. You may need to put your own API key in as an arg.\n",
    "a = MPRester()\n",
    "\n",
    "#Entries are the basic unit for thermodynamic and other analyses in pymatgen.\n",
    "#This gets all entries belonging to the Ca-C-O system.\n",
    "entries = a.get_entries_in_chemsys(['Ca', 'C', 'O'])\n",
    "\n",
    "#With entries, you can do many sophisticated analyses, like creating phase diagrams.\n",
    "pd = PhaseDiagram(entries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the phase diagram\n",
    "\n",
    "To plot a phase diagram, we send our phase diagram object into the PDPlotter class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OHSlevLhH7lvYBAcHM3XqVKs8c+ZMa5NfEfF+SopEPGTixIkkJiYCUKNGDYYM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1159c37f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's show all phases, including unstable ones\n",
    "plotter = PDPlotter(pd, show_unstable=True)\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating energy above hull and other phase equilibria properties\n",
    "\n",
    "To perform more sophisticated analyses, use the PDAnalyzer object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Materials ID Composition     Ehull                           Decomposition\n",
      "0        mp-166          Ca  0.013329                                1.00 Ca2\n",
      "1      mp-10683          Ca  0.396161                                1.00 Ca2\n",
      "2         mp-21          Ca  0.013583                                1.00 Ca2\n",
      "3         mp-45          Ca  0.000508                                1.00 Ca2\n",
      "4    mp-1008498          Ca  0.152771                                1.00 Ca2\n",
      "5        mp-132          Ca  0.000000                                1.00 Ca2\n",
      "6     mp-632329           C  0.110332                                1.00 C16\n",
      "7     mp-568028           C  0.528945                                1.00 C16\n",
      "8    mp-1008374           C  0.437262                                1.00 C16\n",
      "9     mp-568286           C  0.000000                                1.00 C16\n",
      "10    mp-611426           C  0.145308                                1.00 C16\n",
      "11    mp-683919           C  0.348592                                1.00 C16\n",
      "12    mp-937760           C  0.003670                                1.00 C16\n",
      "13    mp-998866           C  2.756837                                1.00 C16\n",
      "14    mp-569567           C  0.174406                                1.00 C16\n",
      "15    mp-667273           C  0.388760                                1.00 C16\n",
      "16    mp-570002           C  0.762539                                1.00 C16\n",
      "17    mp-990448           C  0.003052                                1.00 C16\n",
      "18    mp-579909           C  0.944071                                1.00 C16\n",
      "19    mp-997182           C  0.003585                                1.00 C16\n",
      "20    mp-568806           C  0.006175                                1.00 C16\n",
      "21   mp-1018088           C  1.299379                                1.00 C16\n",
      "22        mp-66           C  0.135631                                1.00 C16\n",
      "23    mp-606949           C  0.003575                                1.00 C16\n",
      "24    mp-680372           C  0.407085                                1.00 C16\n",
      "25    mp-616440           C  0.140564                                1.00 C16\n",
      "26    mp-568410           C  0.506378                                1.00 C16\n",
      "27       mp-169           C  0.004413                                1.00 C16\n",
      "28    mp-624889           C  1.255021                                1.00 C16\n",
      "29    mp-569517           C  0.145186                                1.00 C16\n",
      "..          ...         ...       ...                                     ...\n",
      "53      mp-2482        CaC2  0.022313                     0.67 C16 + 0.33 Ca2\n",
      "54   mp-1009655        CaC2  0.743834                     0.67 C16 + 0.33 Ca2\n",
      "55    mp-570697        CaC2  0.762125                     0.67 C16 + 0.33 Ca2\n",
      "56   mp-1022724        CaC4  0.330530                     0.80 C16 + 0.20 Ca2\n",
      "57    mp-545512         CaO  0.065085                             1.00 Ca1 O1\n",
      "58    mp-634859        CaO2  0.007227                   0.33 O8 + 0.67 Ca1 O1\n",
      "59      mp-2605         CaO  0.000000                             1.00 Ca1 O1\n",
      "60     mp-11725         CO2  0.011643                              1.00 C4 O8\n",
      "61    mp-995198         CO2  0.323686                              1.00 C4 O8\n",
      "62    mp-995196         CO3  3.078212                    0.75 C4 O8 + 0.25 O8\n",
      "63    mp-556660          CO  0.692016                   0.75 C4 O8 + 0.25 C16\n",
      "64   mp-1001594        C4O3  0.241118                   0.64 C4 O8 + 0.36 C16\n",
      "65    mp-556034         CO2  0.028775                              1.00 C4 O8\n",
      "66    mp-995224         CO2  0.302425                              1.00 C4 O8\n",
      "67    mp-644607         CO2  0.028281                              1.00 C4 O8\n",
      "68     mp-11875          CO  0.668938                   0.75 C4 O8 + 0.25 C16\n",
      "69    mp-561848        C3O2  0.513353                   0.60 C4 O8 + 0.40 C16\n",
      "70     mp-20066         CO2  0.000000                              1.00 C4 O8\n",
      "71    mp-995183          CO  0.559450                   0.75 C4 O8 + 0.25 C16\n",
      "72    mp-641635       CaCO3  1.368847                          1.00 Ca2 C2 O6\n",
      "73    mp-560265       CaCO3  0.008073                          1.00 Ca2 C2 O6\n",
      "74      mp-4626       CaCO3  0.024281                          1.00 Ca2 C2 O6\n",
      "75      mp-3953       CaCO3  0.000000                          1.00 Ca2 C2 O6\n",
      "76    mp-548403       CaCO3  0.312190                          1.00 Ca2 C2 O6\n",
      "77    mp-553939       CaCO3  0.013584                          1.00 Ca2 C2 O6\n",
      "78    mp-556235       CaCO3  0.002015                          1.00 Ca2 C2 O6\n",
      "79    mp-558543    Ca(CO2)2  0.046302  0.21 C4 O8 + 0.71 Ca2 C2 O6 + 0.07 C16\n",
      "80    mp-561412       CaCO3  0.007359                          1.00 Ca2 C2 O6\n",
      "81    mp-696740       CaCO3  2.017768                          1.00 Ca2 C2 O6\n",
      "82      mp-3205       CaCO3  0.169799                          1.00 Ca2 C2 O6\n",
      "\n",
      "[83 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "\n",
    "data = collections.defaultdict(list)\n",
    "for e in entries:\n",
    "    decomp, ehull = pd.get_decomp_and_e_above_hull(e)\n",
    "    data[\"Materials ID\"].append(e.entry_id)\n",
    "    data[\"Composition\"].append(e.composition.reduced_formula)\n",
    "    data[\"Ehull\"].append(ehull)    \n",
    "    data[\"Decomposition\"].append(\" + \".join([\"%.2f %s\" % (v, k.composition.formula) for k, v in decomp.items()]))\n",
    "\n",
    "from pandas import DataFrame\n",
    "df = DataFrame(data, columns=[\"Materials ID\", \"Composition\", \"Ehull\", \"Decomposition\"])\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}