pymatgen/Epitaxial Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the best substrate for BiFeO3\n",
    "Let's find the best substrate for a Bismuth Ferrate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pymatgen.matproj.rest import MPRester\n",
    "mpr = MPRester()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "film = mpr.get_structure_by_material_id(\"mp-645159\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to get the elastic tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticTensor([[[[ 242.    0.    0.]\n",
       "   [   0.  121.    0.]\n",
       "   [   0.    0.  119.]]\n",
       "\n",
       "  [[   0.   90.    0.]\n",
       "   [  90.    0.    0.]\n",
       "   [   0.    0.    0.]]\n",
       "\n",
       "  [[   0.    0.   60.]\n",
       "   [   0.    0.    0.]\n",
       "   [  60.    0.    0.]]]\n",
       "\n",
       "\n",
       " [[[   0.   90.    0.]\n",
       "   [  90.    0.    0.]\n",
       "   [   0.    0.    0.]]\n",
       "\n",
       "  [[ 121.    0.    0.]\n",
       "   [   0.  243.    0.]\n",
       "   [   0.    0.  119.]]\n",
       "\n",
       "  [[   0.    0.    0.]\n",
       "   [   0.    0.   78.]\n",
       "   [   0.   78.    0.]]]\n",
       "\n",
       "\n",
       " [[[   0.    0.   60.]\n",
       "   [   0.    0.    0.]\n",
       "   [  60.    0.    0.]]\n",
       "\n",
       "  [[   0.    0.    0.]\n",
       "   [   0.    0.   78.]\n",
       "   [   0.   78.    0.]]\n",
       "\n",
       "  [[ 119.    0.    0.]\n",
       "   [   0.  119.    0.]\n",
       "   [   0.    0.  255.]]]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pymatgen.analysis.elasticity.elastic import ElasticTensor\n",
    "elac = mpr.query({\"material_id\": \"mp-645159\"},['elasticity'])[0]\n",
    "tensor = ElasticTensor.from_voigt(elac['elasticity']['elastic_tensor'])\n",
    "tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get a list of substrates to consider. Materials Project has collected a set of the most accessible substrates and their corresponding MPids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mp-2920',\n",
       " 'mp-661',\n",
       " 'mp-20194',\n",
       " 'mp-2534',\n",
       " 'mp-1029',\n",
       " 'mp-6930',\n",
       " 'mp-23193',\n",
       " 'mp-31120',\n",
       " 'mp-20305',\n",
       " 'mp-2490',\n",
       " 'mp-22693',\n",
       " 'mp-3614',\n",
       " 'mp-20351',\n",
       " 'mp-905',\n",
       " 'mp-2133',\n",
       " 'mp-2201',\n",
       " 'mp-19717',\n",
       " 'mp-602',\n",
       " 'mp-23',\n",
       " 'mp-34202',\n",
       " 'mp-1943',\n",
       " 'mp-984',\n",
       " 'mp-541837',\n",
       " 'mp-1434',\n",
       " 'mp-134',\n",
       " 'mp-5854',\n",
       " 'mp-406',\n",
       " 'mp-2125',\n",
       " 'mp-7631',\n",
       " 'mp-3666',\n",
       " 'mp-19306',\n",
       " 'mp-1265',\n",
       " 'mp-2657',\n",
       " 'mp-23560',\n",
       " 'mp-66',\n",
       " 'mp-31119',\n",
       " 'mp-153',\n",
       " 'mp-23959',\n",
       " 'mp-1143',\n",
       " 'mp-1190',\n",
       " 'mp-21276',\n",
       " 'mp-804',\n",
       " 'mp-672',\n",
       " 'mp-2858',\n",
       " 'mp-20012',\n",
       " 'mp-3731',\n",
       " 'mp-5690',\n",
       " 'mp-19387',\n",
       " 'mp-3427',\n",
       " 'mp-3196',\n",
       " 'mp-11714',\n",
       " 'mp-1634',\n",
       " 'mp-886',\n",
       " 'mp-149',\n",
       " 'mp-81',\n",
       " 'mp-2691',\n",
       " 'mp-224',\n",
       " 'mp-48',\n",
       " 'mp-1821',\n",
       " 'mp-3792',\n",
       " 'mp-22862',\n",
       " 'mp-390',\n",
       " 'mp-23352',\n",
       " 'mp-3953',\n",
       " 'mp-4651',\n",
       " 'mp-19648',\n",
       " 'mp-8062',\n",
       " 'mp-124',\n",
       " 'mp-614603',\n",
       " 'mp-1249',\n",
       " 'mp-19133',\n",
       " 'mp-3536',\n",
       " 'mp-1156',\n",
       " 'mp-30',\n",
       " 'mp-542812',\n",
       " 'mp-32',\n",
       " 'mp-5986',\n",
       " 'mp-1138',\n",
       " 'mp-2741',\n",
       " 'mp-24972',\n",
       " 'mp-2176']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpr.get_all_substrates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pot_substrates = [mpr.get_structure_by_material_id(s) for s in mpr.get_all_substrates()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full Formula (Al2 N2)\n",
      "Reduced Formula: AlN\n",
      "abc   :   3.128588   3.128588   5.016955\n",
      "angles:  90.000000  90.000000 120.000000\n",
      "Sites (4)\n",
      "  #  SP           a         b         c    coordination_no  forces\n",
      "---  ----  --------  --------  --------  -----------------  ----------------------\n",
      "  0  N     0.666667  0.333333  0.880713                  4  [0.0, 0.0, 0.0007832]\n",
      "  1  N     0.333333  0.666667  0.380713                  4  [0.0, 0.0, 0.0007832]\n",
      "  2  Al    0.666667  0.333333  0.499287                  4  [0.0, 0.0, -0.0007832]\n",
      "  3  Al    0.333333  0.666667  0.999287                  4  [0.0, 0.0, -0.0007832]\n"
     ]
    }
   ],
   "source": [
    "print(pot_substrates[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to initialize the substrate analyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymatgen.analysis.substrate_analyzer import SubstrateAnalyzer\n",
    "sa = SubstrateAnalyzer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analyzer can be used to calculate matches between a substrates and films"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_matches = []\n",
    "for substrate in pot_substrates[:10]:\n",
    "    matches = list(sa.calculate(substrate,film,tensor,lowest=True))\n",
    "    for m in matches:\n",
    "        m['composition'] = str(substrate.composition.reduced_formula)\n",
    "    all_matches.extend(matches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_matches)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEKCAYAAADAVygjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff2eb534e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "for i in all_matches:\n",
    "    plt.scatter(i['elastic_energy'],i['match_area'])\n",
    "plt.xlabel(\"Elastic Energy (eV)\")\n",
    "plt.ylabel(r\"Matching Area ($\\AA^2$)\")\n",
    "plt.xlim(0,0.005)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Our previous analysis shows that the lower left corner is preferred. So let's look at matches with matching area <"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Material: CeO2 Substrate Face: [0, 1, 0]  Film Direction: [1, 0, -1]  Strain: 0.012856399831028797\n",
      "Material: GaAs Substrate Face: [1, 0, 0]  Film Direction: [1, 0, -1]  Strain: 0.018005629745282737\n",
      "Material: DyScO3 Substrate Face: [0, 0, 1]  Film Direction: [0, 0, 1]  Strain: 0.006456864634338208\n",
      "Material: DyScO3 Substrate Face: [0, 1, 0]  Film Direction: [0, 1, 0]  Strain: 0.011114725727691326\n",
      "Material: DyScO3 Substrate Face: [1, 0, 0]  Film Direction: [1, 0, 0]  Strain: 0.006888529056243525\n",
      "Material: GaP Substrate Face: [0, 1, 0]  Film Direction: [1, 0, -1]  Strain: 0.010095340220019764\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for m in all_matches:\n",
    "    if m['match_area'] < 50.0 and m['elastic_energy'] < 0.002:\n",
    "        print(\"Material: {} Substrate Face: {}  Film Direction: {}  Strain: {}\".format(m['composition'],\n",
    "                                                                                      m['sub_miller'],\n",
    "                                                                                      m['film_miller'],\n",
    "                                                                                      m['strain']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}