├── .gitignore
├── README.md
└── notebooks
    ├── 1 - Structure Generation.ipynb
    ├── 2 - Phase and Electrochemical Stability.ipynb
    ├── 3 - Diffusivity and Ionic Conductivity.ipynb
    ├── CHGCAR.vasp
    ├── EntryWithCollCode418490.cif
    ├── Isosurface_800K_0.png
    ├── POTCAR.gz
    ├── diffusion_analyzer
        ├── 1000.json
        ├── 1200.json
        ├── 600.json
        └── 800.json
    ├── lpo_entries.json
    └── vasprun.xml.relax2.gz


/.gitignore:
--------------------------------------------------------------------------------
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 3 | *.py[cod]
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90 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Introduction
2 | 
3 | This repository contains the Jupyter notebooks and data for our Chemistry of Materials article "Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors". 
4 | 
5 | In this article, we present a detailed exposition of how first principles methods can be used to guide alkali superionic conductor (ASIC) study and design. Using the argyrodite Li6PS5Cl as a case study, we demonstrate how modern information technology (IT) infrastructure and software tools can facilitate the assessment of alkali superionic conductors in terms of various critical properties of interest such as phase and electrochemical stability, and ionic conductivity. The emphasis is on well-documented, reproducible analysis code that can be readily generalized to other materials systems and design problems. 
6 | 
7 | You can find the article at the [Materials Virtual Lab's publication page](http://materialsvirtuallab.org/publications) or at the Chemistry of Materials [website](http://pubs.acs.org/doi/abs/10.1021/acs.chemmater.6b02648).
8 | 


--------------------------------------------------------------------------------
/notebooks/1 - Structure Generation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Introduction\n",
  8 |     "\n",
  9 |     "This notebook demonstrates how to perform structure enumeration using Python Materials Genomics (pymatgen).\n",
 10 |     "\n",
 11 |     "Let's start by importing some modules and classes that we will be using."
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 1,
 17 |    "metadata": {
 18 |     "collapsed": true
 19 |    },
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "from pymatgen import Structure\n",
 23 |     "from pymatgen.symmetry.analyzer import SpacegroupAnalyzer\n",
 24 |     "from pymatgen.transformations.advanced_transformations import EnumerateStructureTransformation\n",
 25 |     "from pymatgen.io.vasp.sets import batch_write_input, MPRelaxSet"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "markdown",
 30 |    "metadata": {},
 31 |    "source": [
 32 |     "# Preparation\n",
 33 |     "\n",
 34 |     "We will first read in the structure from crystallographic information file (CIF) found in ICSD."
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": 2,
 40 |    "metadata": {
 41 |     "collapsed": false
 42 |    },
 43 |    "outputs": [
 44 |     {
 45 |      "name": "stdout",
 46 |      "output_type": "stream",
 47 |      "text": [
 48 |       "Full Formula (Li26.88 P4 S20 Cl4)\n",
 49 |       "Reduced Formula: Li26.88P4S20Cl4\n",
 50 |       "abc   :   9.859000   9.859000   9.859000\n",
 51 |       "angles:  90.000000  90.000000  90.000000\n",
 52 |       "Sites (76)\n",
 53 |       "  #  SP               a        b        c\n",
 54 |       "---  ---------  -------  -------  -------\n",
 55 |       "  0  Li+:0.560  0.3148   0.982    0.3148\n",
 56 |       "  1  Li+:0.560  0.982    0.6852   0.6852\n",
 57 |       "  2  Li+:0.560  0.6852   0.3148   0.018\n",
 58 |       "  3  Li+:0.560  0.3148   0.6852   0.018\n",
 59 |       "  4  Li+:0.560  0.982    0.3148   0.3148\n",
 60 |       "  5  Li+:0.560  0.6852   0.982    0.6852\n",
 61 |       "  6  Li+:0.560  0.3148   0.018    0.6852\n",
 62 |       "  7  Li+:0.560  0.6852   0.6852   0.982\n",
 63 |       "  8  Li+:0.560  0.3148   0.3148   0.982\n",
 64 |       "  9  Li+:0.560  0.6852   0.018    0.3148\n",
 65 |       " 10  Li+:0.560  0.018    0.6852   0.3148\n",
 66 |       " 11  Li+:0.560  0.018    0.3148   0.6852\n",
 67 |       " 12  Li+:0.560  0.3148   0.482    0.8148\n",
 68 |       " 13  Li+:0.560  0.982    0.1852   0.1852\n",
 69 |       " 14  Li+:0.560  0.6852   0.8148   0.518\n",
 70 |       " 15  Li+:0.560  0.3148   0.1852   0.518\n",
 71 |       " 16  Li+:0.560  0.982    0.8148   0.8148\n",
 72 |       " 17  Li+:0.560  0.6852   0.482    0.1852\n",
 73 |       " 18  Li+:0.560  0.3148   0.518    0.1852\n",
 74 |       " 19  Li+:0.560  0.6852   0.1852   0.482\n",
 75 |       " 20  Li+:0.560  0.3148   0.8148   0.482\n",
 76 |       " 21  Li+:0.560  0.6852   0.518    0.8148\n",
 77 |       " 22  Li+:0.560  0.018    0.1852   0.8148\n",
 78 |       " 23  Li+:0.560  0.018    0.8148   0.1852\n",
 79 |       " 24  Li+:0.560  0.8148   0.982    0.8148\n",
 80 |       " 25  Li+:0.560  0.482    0.6852   0.1852\n",
 81 |       " 26  Li+:0.560  0.1852   0.3148   0.518\n",
 82 |       " 27  Li+:0.560  0.8148   0.6852   0.518\n",
 83 |       " 28  Li+:0.560  0.482    0.3148   0.8148\n",
 84 |       " 29  Li+:0.560  0.1852   0.982    0.1852\n",
 85 |       " 30  Li+:0.560  0.8148   0.018    0.1852\n",
 86 |       " 31  Li+:0.560  0.1852   0.6852   0.482\n",
 87 |       " 32  Li+:0.560  0.8148   0.3148   0.482\n",
 88 |       " 33  Li+:0.560  0.1852   0.018    0.8148\n",
 89 |       " 34  Li+:0.560  0.518    0.6852   0.8148\n",
 90 |       " 35  Li+:0.560  0.518    0.3148   0.1852\n",
 91 |       " 36  Li+:0.560  0.8148   0.482    0.3148\n",
 92 |       " 37  Li+:0.560  0.482    0.1852   0.6852\n",
 93 |       " 38  Li+:0.560  0.1852   0.8148   0.018\n",
 94 |       " 39  Li+:0.560  0.8148   0.1852   0.018\n",
 95 |       " 40  Li+:0.560  0.482    0.8148   0.3148\n",
 96 |       " 41  Li+:0.560  0.1852   0.482    0.6852\n",
 97 |       " 42  Li+:0.560  0.8148   0.518    0.6852\n",
 98 |       " 43  Li+:0.560  0.1852   0.1852   0.982\n",
 99 |       " 44  Li+:0.560  0.8148   0.8148   0.982\n",
100 |       " 45  Li+:0.560  0.1852   0.518    0.3148\n",
101 |       " 46  Li+:0.560  0.518    0.1852   0.3148\n",
102 |       " 47  Li+:0.560  0.518    0.8148   0.6852\n",
103 |       " 48  P5+        0.5      0        0\n",
104 |       " 49  P5+        0        0        0.5\n",
105 |       " 50  P5+        0        0.5      0\n",
106 |       " 51  P5+        0.5      0.5      0.5\n",
107 |       " 52  S2-        0.25     0.75     0.25\n",
108 |       " 53  S2-        0.75     0.75     0.75\n",
109 |       " 54  S2-        0.75     0.25     0.25\n",
110 |       " 55  S2-        0.25     0.25     0.75\n",
111 |       " 56  S2-        0.38053  0.11947  0.11947\n",
112 |       " 57  S2-        0.11947  0.88053  0.61947\n",
113 |       " 58  S2-        0.88053  0.38053  0.88053\n",
114 |       " 59  S2-        0.38053  0.88053  0.88053\n",
115 |       " 60  S2-        0.11947  0.38053  0.11947\n",
116 |       " 61  S2-        0.88053  0.11947  0.61947\n",
117 |       " 62  S2-        0.11947  0.11947  0.38053\n",
118 |       " 63  S2-        0.88053  0.61947  0.11947\n",
119 |       " 64  S2-        0.11947  0.61947  0.88053\n",
120 |       " 65  S2-        0.88053  0.88053  0.38053\n",
121 |       " 66  S2-        0.61947  0.11947  0.88053\n",
122 |       " 67  S2-        0.61947  0.88053  0.11947\n",
123 |       " 68  S2-        0.38053  0.61947  0.61947\n",
124 |       " 69  S2-        0.38053  0.38053  0.38053\n",
125 |       " 70  S2-        0.61947  0.61947  0.38053\n",
126 |       " 71  S2-        0.61947  0.38053  0.61947\n",
127 |       " 72  Cl-        0        0        0\n",
128 |       " 73  Cl-        0        0.5      0.5\n",
129 |       " 74  Cl-        0.5      0        0.5\n",
130 |       " 75  Cl-        0.5      0.5      0\n"
131 |      ]
132 |     }
133 |    ],
134 |    "source": [
135 |     "structure = Structure.from_file(\"EntryWithCollCode418490.cif\")\n",
136 |     "print(structure)"
137 |    ]
138 |   },
139 |   {
140 |    "cell_type": "markdown",
141 |    "metadata": {},
142 |    "source": [
143 |     "From the above, we see that the reported experimental structure has Li disorder. The occupancy of Li is higher than what would be expected for a Li6PS5Cl nominal composition. We will first adjust the composition by setting the Li occupancies to 0.5 to obtain stoichiometric charge-balanced Li6PS5Cl."
144 |    ]
145 |   },
146 |   {
147 |    "cell_type": "code",
148 |    "execution_count": 3,
149 |    "metadata": {
150 |     "collapsed": false
151 |    },
152 |    "outputs": [
153 |     {
154 |      "name": "stdout",
155 |      "output_type": "stream",
156 |      "text": [
157 |       "The composition after adjustments is Li6PS5Cl.\n"
158 |      ]
159 |     }
160 |    ],
161 |    "source": [
162 |     "# loop over all sites in the structure\n",
163 |     "for i, site in enumerate(structure):\n",
164 |     "    # change the occupancy of Li+ disordered sites to 0.5\n",
165 |     "    if not site.is_ordered:\n",
166 |     "        structure[i] = {\"Li+\": 0.5}\n",
167 |     "print(\"The composition after adjustments is %s.\" % structure.composition.reduced_formula)"
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "markdown",
172 |    "metadata": {},
173 |    "source": [
174 |     "To keep the number of orderings manageable, we will perform enumeration only on the primitive cell. The primitive cell can be obtained using the *SpacegroupAnalyzer*."
175 |    ]
176 |   },
177 |   {
178 |    "cell_type": "code",
179 |    "execution_count": 4,
180 |    "metadata": {
181 |     "collapsed": false
182 |    },
183 |    "outputs": [
184 |     {
185 |      "name": "stdout",
186 |      "output_type": "stream",
187 |      "text": [
188 |       "Full Formula (Li6 P1 S5 Cl1)\n",
189 |       "Reduced Formula: Li6PS5Cl\n",
190 |       "abc   :   6.971366   6.971366   6.971366\n",
191 |       "angles:  60.000000  60.000000  60.000000\n",
192 |       "Sites (19)\n",
193 |       "  #  SP               a        b        c\n",
194 |       "---  ---------  -------  -------  -------\n",
195 |       "  0  Li+:0.500  0.3616   0.768    0.768\n",
196 |       "  1  Li+:0.500  0.1024   0.768    0.3616\n",
197 |       "  2  Li+:0.500  0.768    0.768    0.1024\n",
198 |       "  3  Li+:0.500  0.768    0.768    0.3616\n",
199 |       "  4  Li+:0.500  0.3616   0.768    0.1024\n",
200 |       "  5  Li+:0.500  0.1024   0.768    0.768\n",
201 |       "  6  Li+:0.500  0.768    0.1024   0.3616\n",
202 |       "  7  Li+:0.500  0.1024   0.3616   0.768\n",
203 |       "  8  Li+:0.500  0.3616   0.1024   0.768\n",
204 |       "  9  Li+:0.500  0.768    0.3616   0.1024\n",
205 |       " 10  Li+:0.500  0.768    0.3616   0.768\n",
206 |       " 11  Li+:0.500  0.768    0.1024   0.768\n",
207 |       " 12  P5+        0.25     0.25     0.25\n",
208 |       " 13  S2-        0        0        0\n",
209 |       " 14  S2-        0.36947  0.36947  0.89159\n",
210 |       " 15  S2-        0.36947  0.36947  0.36947\n",
211 |       " 16  S2-        0.89159  0.36947  0.36947\n",
212 |       " 17  S2-        0.36947  0.89159  0.36947\n",
213 |       " 18  Cl-        0.75     0.75     0.75\n"
214 |      ]
215 |     }
216 |    ],
217 |    "source": [
218 |     "analyzer = SpacegroupAnalyzer(structure)\n",
219 |     "prim_cell = analyzer.find_primitive()\n",
220 |     "print(prim_cell)"
221 |    ]
222 |   },
223 |   {
224 |    "cell_type": "markdown",
225 |    "metadata": {},
226 |    "source": [
227 |     "# Enumerate structures using enumlib\n",
228 |     "\n",
229 |     "We will use the *EnumerateStructureTransformation* class to enumerate all symmetrically distinct structures. *EnumerateStructureTransformation* is a user-friendly wrapper around enumlib, a fortran library to generate derivative structures written by Hart et al."
230 |    ]
231 |   },
232 |   {
233 |    "cell_type": "code",
234 |    "execution_count": 5,
235 |    "metadata": {
236 |     "collapsed": false
237 |    },
238 |    "outputs": [
239 |     {
240 |      "name": "stdout",
241 |      "output_type": "stream",
242 |      "text": [
243 |       "48 structures returned.\n"
244 |      ]
245 |     }
246 |    ],
247 |    "source": [
248 |     "enum = EnumerateStructureTransformation()\n",
249 |     "enumerated = enum.apply_transformation(prim_cell, 100)  # return no more than 100 structures\n",
250 |     "structures = [d[\"structure\"] for d in enumerated]  \n",
251 |     "print(\"%d structures returned.\" % len(structures))"
252 |    ]
253 |   },
254 |   {
255 |    "cell_type": "markdown",
256 |    "metadata": {},
257 |    "source": [
258 |     "# VASP input generation\n",
259 |     "\n",
260 |     "Pymatgen has useful classes for batch generating VASP input files that use parameters that are compatible with those used in the Materials Project. These parameters have been well-tested over a large database of structures in different chemistries. Using the same parameters also allow the energies computed to be compared with those in the Materials Project database for phase stability and other analyses."
261 |    ]
262 |   },
263 |   {
264 |    "cell_type": "code",
265 |    "execution_count": 6,
266 |    "metadata": {
267 |     "collapsed": true
268 |    },
269 |    "outputs": [],
270 |    "source": [
271 |     "batch_write_input(structures, vasp_input_set=MPRelaxSet, output_dir=\"Li6PS5Cl_orderings\")"
272 |    ]
273 |   }
274 |  ],
275 |  "metadata": {
276 |   "kernelspec": {
277 |    "display_name": "Python 3",
278 |    "language": "python",
279 |    "name": "python3"
280 |   },
281 |   "language_info": {
282 |    "codemirror_mode": {
283 |     "name": "ipython",
284 |     "version": 3
285 |    },
286 |    "file_extension": ".py",
287 |    "mimetype": "text/x-python",
288 |    "name": "python",
289 |    "nbconvert_exporter": "python",
290 |    "pygments_lexer": "ipython3",
291 |    "version": "3.5.1"
292 |   }
293 |  },
294 |  "nbformat": 4,
295 |  "nbformat_minor": 0
296 | }
297 | 


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/notebooks/2 - Phase and Electrochemical Stability.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Introduction\n",
  8 |     "\n",
  9 |     "This notebook demonstrates how to perform phase and electrochemical assessments starting from a VASP calculation using Python Materials Genomics (pymatgen) and the Materials Project database (via the Materials API). \n",
 10 |     "\n",
 11 |     "Let's start by importing some modules and classes that we will be using."
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 1,
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "%matplotlib inline\n",
 21 |     "import warnings\n",
 22 |     "\n",
 23 |     "warnings.simplefilter(\"ignore\")\n",
 24 |     "\n",
 25 |     "from pymatgen import MPRester, Composition, Element\n",
 26 |     "from pymatgen.io.vasp import Vasprun\n",
 27 |     "from pymatgen.analysis.phase_diagram import PhaseDiagram, CompoundPhaseDiagram\n",
 28 |     "from pymatgen.analysis.phase_diagram import PDPlotter\n",
 29 |     "from pymatgen.entries.computed_entries import ComputedEntry\n",
 30 |     "from pymatgen.entries.compatibility import MaterialsProjectCompatibility\n",
 31 |     "from pymatgen.util.plotting import pretty_plot\n",
 32 |     "import json\n",
 33 |     "import re\n",
 34 |     "import palettable\n",
 35 |     "import matplotlib as mpl"
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "markdown",
 40 |    "metadata": {},
 41 |    "source": [
 42 |     "# Preparation\n",
 43 |     "\n",
 44 |     "We will first read the results from the *vasprun.xml* output file from our VASP calculations. Only the lowest energy result is used here."
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 2,
 50 |    "metadata": {},
 51 |    "outputs": [],
 52 |    "source": [
 53 |     "vasprun = Vasprun(\"vasprun.xml.relax2.gz\")\n",
 54 |     "# include structure so proper correction can be applied for oxides and sulfides\n",
 55 |     "entry = vasprun.get_computed_entry(inc_structure=True)"
 56 |    ]
 57 |   },
 58 |   {
 59 |    "cell_type": "markdown",
 60 |    "metadata": {},
 61 |    "source": [
 62 |     "To construct the phase diagram, we need all entries in the Li-P-S-Cl chemical space. We will use the *MPRester* class to obtain these entries from the Materials Project via the Materials API."
 63 |    ]
 64 |   },
 65 |   {
 66 |    "cell_type": "code",
 67 |    "execution_count": 3,
 68 |    "metadata": {},
 69 |    "outputs": [],
 70 |    "source": [
 71 |     "rester = MPRester()\n",
 72 |     "mp_entries = rester.get_entries_in_chemsys([\"Li\", \"P\", \"S\", \"Cl\"])"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "markdown",
 77 |    "metadata": {},
 78 |    "source": [
 79 |     "In addition to all the MP entries, here we also load the computed entries of O/S substituted Li-P-O tenary compounds."
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "code",
 84 |    "execution_count": 4,
 85 |    "metadata": {},
 86 |    "outputs": [],
 87 |    "source": [
 88 |     "with open(\"lpo_entries.json\") as f:\n",
 89 |     "    lpo_data = json.load(f)\n",
 90 |     "lpo_entries = [ComputedEntry.from_dict(d) for d in lpo_data]"
 91 |    ]
 92 |   },
 93 |   {
 94 |    "cell_type": "markdown",
 95 |    "metadata": {},
 96 |    "source": [
 97 |     "Next, we need to combine all the entries and postprocess them using *MaterialsProjectCompatibility*. This postprocessing step corrects the energies to account for well-known DFT errors, e.g., in the sulfur binding energy."
 98 |    ]
 99 |   },
100 |   {
101 |    "cell_type": "code",
102 |    "execution_count": 5,
103 |    "metadata": {},
104 |    "outputs": [],
105 |    "source": [
106 |     "compatibility = MaterialsProjectCompatibility()\n",
107 |     "entry = compatibility.process_entry(entry)\n",
108 |     "entries = compatibility.process_entries([entry] + mp_entries + lpo_entries)"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "markdown",
113 |    "metadata": {
114 |     "collapsed": true
115 |    },
116 |    "source": [
117 |     "# Phase diagram construction\n",
118 |     "\n",
119 |     "The phase diagram can then be constructed using the *PhaseDiagram* class, and plotted using the *PDPlotter* class."
120 |    ]
121 |   },
122 |   {
123 |    "cell_type": "code",
124 |    "execution_count": 6,
125 |    "metadata": {},
126 |    "outputs": [
127 |     {
128 |      "data": {
129 |       "image/png": 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\n",
130 |       "text/plain": [
131 |        "<Figure size 432x288 with 1 Axes>"
132 |       ]
133 |      },
134 |      "metadata": {
135 |       "needs_background": "light"
136 |      },
137 |      "output_type": "display_data"
138 |     }
139 |    ],
140 |    "source": [
141 |     "pd = PhaseDiagram(entries)\n",
142 |     "plotter = PDPlotter(pd)\n",
143 |     "plotter.show()"
144 |    ]
145 |   },
146 |   {
147 |    "cell_type": "markdown",
148 |    "metadata": {},
149 |    "source": [
150 |     "We may observe from the above phase diagram that Li6PS5Cl is not a stable phase (red nodes) in the calculated 0K phase diagram.\n",
151 |     "\n",
152 |     "The pseudo-ternary Li2S-P2S5-LiCl is constructed using the *CompoundPhaseDiagram* class."
153 |    ]
154 |   },
155 |   {
156 |    "cell_type": "code",
157 |    "execution_count": 7,
158 |    "metadata": {},
159 |    "outputs": [
160 |     {
161 |      "data": {
162 |       "image/png": 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\n",
163 |       "text/plain": [
164 |        "<Figure size 576x432 with 1 Axes>"
165 |       ]
166 |      },
167 |      "metadata": {},
168 |      "output_type": "display_data"
169 |     }
170 |    ],
171 |    "source": [
172 |     "cpd = CompoundPhaseDiagram(entries, \n",
173 |     "                           [Composition(\"P2S5\"), Composition(\"Li2S\"), Composition(\"LiCl\")])\n",
174 |     "cplotter = PDPlotter(cpd, show_unstable=True)\n",
175 |     "cplotter.show()"
176 |    ]
177 |   },
178 |   {
179 |    "cell_type": "markdown",
180 |    "metadata": {},
181 |    "source": [
182 |     "# Calculating $E_{\\rm hull}$ of Li6PS5Cl\n",
183 |     "\n",
184 |     "We may evaluate the $E_{\\rm hull}$ of Li6PS5Cl using the *PDAnalyzer*."
185 |    ]
186 |   },
187 |   {
188 |    "cell_type": "code",
189 |    "execution_count": 8,
190 |    "metadata": {},
191 |    "outputs": [
192 |     {
193 |      "name": "stdout",
194 |      "output_type": "stream",
195 |      "text": [
196 |       "The energy above hull of Li6PS5Cl is 0.029 eV/atom.\n"
197 |      ]
198 |     }
199 |    ],
200 |    "source": [
201 |     "ehull = pd.get_e_above_hull(entry)\n",
202 |     "print(\"The energy above hull of Li6PS5Cl is %.3f eV/atom.\" % ehull)"
203 |    ]
204 |   },
205 |   {
206 |    "cell_type": "markdown",
207 |    "metadata": {
208 |     "collapsed": true
209 |    },
210 |    "source": [
211 |     "# Electrochemical Stability\n",
212 |     "\n",
213 |     "The electrochemical stability can be assessed using a similar phase diagram approach, but using the lithium grand potential instead of the internal energy.\n",
214 |     "\n",
215 |     "First, we need to identify a reference for lithium chemical potential using the bulk Li energy $\\mu_{\\rm Li}^0$."
216 |    ]
217 |   },
218 |   {
219 |    "cell_type": "code",
220 |    "execution_count": 9,
221 |    "metadata": {},
222 |    "outputs": [],
223 |    "source": [
224 |     "li_entries = [e for e in entries if e.composition.reduced_formula == \"Li\"]\n",
225 |     "uli0 = min(li_entries, key=lambda e: e.energy_per_atom).energy_per_atom"
226 |    ]
227 |   },
228 |   {
229 |    "cell_type": "markdown",
230 |    "metadata": {},
231 |    "source": [
232 |     "The *PDAnalyzer* class provides a quick way to plot the phase diagram at a particular composition (e.g., Li6PS5Cl) as a function of lithium chemical potential called *get_element_profile*."
233 |    ]
234 |   },
235 |   {
236 |    "cell_type": "code",
237 |    "execution_count": 10,
238 |    "metadata": {},
239 |    "outputs": [
240 |     {
241 |      "name": "stdout",
242 |      "output_type": "stream",
243 |      "text": [
244 |       "Voltage: -0.0 V\n",
245 |       "4 Li6PS5Cl + 32 Li -> 4 Li3P + 4 LiCl + 20 Li2S\n",
246 |       "\n",
247 |       "Voltage: 0.8697028629166668 V\n",
248 |       "4 Li6PS5Cl + 24 Li -> 4 LiP + 4 LiCl + 20 Li2S\n",
249 |       "\n",
250 |       "Voltage: 0.9324093885416653 V\n",
251 |       "4 Li6PS5Cl + 21.71 Li -> 0.5714 Li3P7 + 4 LiCl + 20 Li2S\n",
252 |       "\n",
253 |       "Voltage: 1.1619996604166705 V\n",
254 |       "4 Li6PS5Cl + 20.57 Li -> 0.5714 LiP7 + 4 LiCl + 20 Li2S\n",
255 |       "\n",
256 |       "Voltage: 1.2717621691666654 V\n",
257 |       "4 Li6PS5Cl + 20 Li -> 4 LiCl + 20 Li2S + 4 P\n",
258 |       "\n",
259 |       "Voltage: 1.7076498754523812 V\n",
260 |       "4 Li6PS5Cl -> 4 Li3PS4 + 4 LiCl + 4 Li2S\n",
261 |       "\n",
262 |       "Voltage: 2.1291348952380966 V\n",
263 |       "4 Li6PS5Cl -> 4 Li3PS4 + LiS4 + 4 LiCl + 7 Li\n",
264 |       "\n",
265 |       "Voltage: 2.368810398840578 V\n",
266 |       "4 Li6PS5Cl -> 1.5 LiS4 + 4 LiCl + 2 P2S7 + 18.5 Li\n",
267 |       "\n",
268 |       "Voltage: 2.886446450666666 V\n",
269 |       "4 Li6PS5Cl -> 0.5 LiS4 + 2 P2S7 + 4 SCl + 23.5 Li\n",
270 |       "\n",
271 |       "Voltage: 3.7828703591666626 V\n",
272 |       "4 Li6PS5Cl -> 2 P2S7 + 2 S + 4 SCl + 24 Li\n",
273 |       "\n"
274 |      ]
275 |     }
276 |    ],
277 |    "source": [
278 |     "el_profile = pd.get_element_profile(Element(\"Li\"), entry.composition)\n",
279 |     "for i, d in enumerate(el_profile):\n",
280 |     "    voltage = -(d[\"chempot\"] - uli0)\n",
281 |     "    print(\"Voltage: %s V\" % voltage)\n",
282 |     "    print(d[\"reaction\"])\n",
283 |     "    print(\"\")"
284 |    ]
285 |   },
286 |   {
287 |    "cell_type": "markdown",
288 |    "metadata": {},
289 |    "source": [
290 |     "This element profile can be plotted as a Li evolution versus voltage using matplotlib as follows."
291 |    ]
292 |   },
293 |   {
294 |    "cell_type": "code",
295 |    "execution_count": 11,
296 |    "metadata": {},
297 |    "outputs": [
298 |     {
299 |      "data": {
300 |       "image/png": 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\n",
301 |       "text/plain": [
302 |        "<Figure size 864x576 with 1 Axes>"
303 |       ]
304 |      },
305 |      "metadata": {},
306 |      "output_type": "display_data"
307 |     }
308 |    ],
309 |    "source": [
310 |     "# Some matplotlib settings to improve the look of the plot.\n",
311 |     "mpl.rcParams['axes.linewidth']=3\n",
312 |     "mpl.rcParams['lines.markeredgewidth']=4\n",
313 |     "mpl.rcParams['lines.linewidth']=3\n",
314 |     "mpl.rcParams['lines.markersize']=15\n",
315 |     "mpl.rcParams['xtick.major.width']=3\n",
316 |     "mpl.rcParams['xtick.major.size']=8\n",
317 |     "mpl.rcParams['xtick.minor.width']=3\n",
318 |     "mpl.rcParams['xtick.minor.size']=4\n",
319 |     "mpl.rcParams['ytick.major.width']=3\n",
320 |     "mpl.rcParams['ytick.major.size']=8\n",
321 |     "mpl.rcParams['ytick.minor.width']=3\n",
322 |     "mpl.rcParams['ytick.minor.size']=4\n",
323 |     "\n",
324 |     "\n",
325 |     "# Plot of Li uptake per formula unit (f.u.) of Li6PS5Cl against voltage vs Li/Li+.\n",
326 |     "\n",
327 |     "colors = palettable.colorbrewer.qualitative.Set1_9.mpl_colors\n",
328 |     "plt = pretty_plot(12, 8)\n",
329 |     "\n",
330 |     "for i, d in enumerate(el_profile):\n",
331 |     "    v = - (d[\"chempot\"] - uli0)\n",
332 |     "    if i != 0:\n",
333 |     "        plt.plot([x2, x2], [y1, d[\"evolution\"] / 4.0], 'k', linewidth=3)\n",
334 |     "    x1 = v\n",
335 |     "    y1 = d[\"evolution\"] / 4.0\n",
336 |     "    if i != len(el_profile) - 1:\n",
337 |     "        x2 = - (el_profile[i + 1][\"chempot\"] - uli0)\n",
338 |     "    else:\n",
339 |     "        x2 = 5.0\n",
340 |     "        \n",
341 |     "    if i in [0, 4, 5, 7]:\n",
342 |     "        products = [re.sub(r\"(\\d+)\", r\"$_{\\1}$\", p.reduced_formula)                     \n",
343 |     "                    for p in d[\"reaction\"].products if p.reduced_formula != \"Li\"]\n",
344 |     "\n",
345 |     "        plt.annotate(\", \".join(products), xy=(v + 0.05, y1 + 0.3), \n",
346 |     "                     fontsize=24, color=colors[0])\n",
347 |     "        \n",
348 |     "        plt.plot([x1, x2], [y1, y1], color=colors[0], linewidth=5)\n",
349 |     "    else:\n",
350 |     "        plt.plot([x1, x2], [y1, y1], 'k', linewidth=3)  \n",
351 |     "\n",
352 |     "plt.xlim((0, 4.0))\n",
353 |     "plt.ylim((-6, 10))\n",
354 |     "plt.xlabel(\"Voltage vs Li/Li$^+$ (V)\")\n",
355 |     "plt.ylabel(\"Li uptake per f.u.\")\n",
356 |     "plt.tight_layout()"
357 |    ]
358 |   },
359 |   {
360 |    "cell_type": "code",
361 |    "execution_count": null,
362 |    "metadata": {},
363 |    "outputs": [],
364 |    "source": []
365 |   }
366 |  ],
367 |  "metadata": {
368 |   "kernelspec": {
369 |    "display_name": "Python 3",
370 |    "language": "python",
371 |    "name": "python3"
372 |   },
373 |   "language_info": {
374 |    "codemirror_mode": {
375 |     "name": "ipython",
376 |     "version": 3
377 |    },
378 |    "file_extension": ".py",
379 |    "mimetype": "text/x-python",
380 |    "name": "python",
381 |    "nbconvert_exporter": "python",
382 |    "pygments_lexer": "ipython3",
383 |    "version": "3.7.7"
384 |   }
385 |  },
386 |  "nbformat": 4,
387 |  "nbformat_minor": 1
388 | }
389 | 


--------------------------------------------------------------------------------
/notebooks/EntryWithCollCode418490.cif:
--------------------------------------------------------------------------------
  1 | 
  2 | #(C) 2016 by FIZ Karlsruhe - Leibniz Institute for Information Infrastructure.  All rights reserved.
  3 | data_418490-ICSD
  4 | _database_code_ICSD 418490
  5 | _audit_creation_date 2008-08-01
  6 | _chemical_name_systematic 'Lithium phosphorus sulfide chloride (6.72/1/5/1)'
  7 | _chemical_formula_structural 'Li6.72 P S5 Cl'
  8 | _chemical_formula_sum 'Cl1 Li6.72 P1 S5'
  9 | _exptl_crystal_density_diffrn 1.89
 10 | _cell_measurement_temperature 296.
 11 | _publ_section_title
 12 | 
 13 | ;
 14 | Li6 P S5 X: A class of crystalline Li-rich solids with unusually high Li(+)
 15 | mobility
 16 | ;
 17 | loop_
 18 | _citation_id
 19 | _citation_journal_full
 20 | _citation_year
 21 | _citation_journal_volume
 22 | _citation_page_first
 23 | _citation_page_last
 24 | _citation_journal_id_ASTM
 25 | primary 'Angewandte Chemie. International Edition' 2008 47 755 758 ACIEF5
 26 | loop_
 27 | _publ_author_name
 28 | 'Deiseroth, H.J.'
 29 | 'Kong Shiaotong'
 30 | 'Eckert, H.'
 31 | 'Vannahme, J.'
 32 | 'Reiner, C.'
 33 | 'Zaiss, T.'
 34 | 'Schlosser, M.'
 35 | _cell_length_a 9.859(2)
 36 | _cell_length_b 9.859(2)
 37 | _cell_length_c 9.859(2)
 38 | _cell_angle_alpha 90.
 39 | _cell_angle_beta 90.
 40 | _cell_angle_gamma 90.
 41 | _cell_volume 958.29
 42 | _cell_formula_units_Z 4
 43 | _symmetry_space_group_name_H-M 'F -4 3 m'
 44 | _symmetry_Int_Tables_number 216
 45 | _refine_ls_R_factor_all 0.0367
 46 | loop_
 47 | _symmetry_equiv_pos_site_id
 48 | _symmetry_equiv_pos_as_xyz
 49 | 1 '-z, -y, x'
 50 | 2 '-y, -x, z'
 51 | 3 '-x, -z, y'
 52 | 4 '-z, -x, y'
 53 | 5 '-y, -z, x'
 54 | 6 '-x, -y, z'
 55 | 7 '-z, y, -x'
 56 | 8 '-y, x, -z'
 57 | 9 '-x, z, -y'
 58 | 10 '-z, x, -y'
 59 | 11 '-y, z, -x'
 60 | 12 '-x, y, -z'
 61 | 13 'z, -y, -x'
 62 | 14 'y, -x, -z'
 63 | 15 'x, -z, -y'
 64 | 16 'z, -x, -y'
 65 | 17 'y, -z, -x'
 66 | 18 'x, -y, -z'
 67 | 19 'z, y, x'
 68 | 20 'y, x, z'
 69 | 21 'x, z, y'
 70 | 22 'z, x, y'
 71 | 23 'y, z, x'
 72 | 24 'x, y, z'
 73 | 25 '-z, -y+1/2, x+1/2'
 74 | 26 '-y, -x+1/2, z+1/2'
 75 | 27 '-x, -z+1/2, y+1/2'
 76 | 28 '-z, -x+1/2, y+1/2'
 77 | 29 '-y, -z+1/2, x+1/2'
 78 | 30 '-x, -y+1/2, z+1/2'
 79 | 31 '-z, y+1/2, -x+1/2'
 80 | 32 '-y, x+1/2, -z+1/2'
 81 | 33 '-x, z+1/2, -y+1/2'
 82 | 34 '-z, x+1/2, -y+1/2'
 83 | 35 '-y, z+1/2, -x+1/2'
 84 | 36 '-x, y+1/2, -z+1/2'
 85 | 37 'z, -y+1/2, -x+1/2'
 86 | 38 'y, -x+1/2, -z+1/2'
 87 | 39 'x, -z+1/2, -y+1/2'
 88 | 40 'z, -x+1/2, -y+1/2'
 89 | 41 'y, -z+1/2, -x+1/2'
 90 | 42 'x, -y+1/2, -z+1/2'
 91 | 43 'z, y+1/2, x+1/2'
 92 | 44 'y, x+1/2, z+1/2'
 93 | 45 'x, z+1/2, y+1/2'
 94 | 46 'z, x+1/2, y+1/2'
 95 | 47 'y, z+1/2, x+1/2'
 96 | 48 'x, y+1/2, z+1/2'
 97 | 49 '-z+1/2, -y, x+1/2'
 98 | 50 '-y+1/2, -x, z+1/2'
 99 | 51 '-x+1/2, -z, y+1/2'
100 | 52 '-z+1/2, -x, y+1/2'
101 | 53 '-y+1/2, -z, x+1/2'
102 | 54 '-x+1/2, -y, z+1/2'
103 | 55 '-z+1/2, y, -x+1/2'
104 | 56 '-y+1/2, x, -z+1/2'
105 | 57 '-x+1/2, z, -y+1/2'
106 | 58 '-z+1/2, x, -y+1/2'
107 | 59 '-y+1/2, z, -x+1/2'
108 | 60 '-x+1/2, y, -z+1/2'
109 | 61 'z+1/2, -y, -x+1/2'
110 | 62 'y+1/2, -x, -z+1/2'
111 | 63 'x+1/2, -z, -y+1/2'
112 | 64 'z+1/2, -x, -y+1/2'
113 | 65 'y+1/2, -z, -x+1/2'
114 | 66 'x+1/2, -y, -z+1/2'
115 | 67 'z+1/2, y, x+1/2'
116 | 68 'y+1/2, x, z+1/2'
117 | 69 'x+1/2, z, y+1/2'
118 | 70 'z+1/2, x, y+1/2'
119 | 71 'y+1/2, z, x+1/2'
120 | 72 'x+1/2, y, z+1/2'
121 | 73 '-z+1/2, -y+1/2, x'
122 | 74 '-y+1/2, -x+1/2, z'
123 | 75 '-x+1/2, -z+1/2, y'
124 | 76 '-z+1/2, -x+1/2, y'
125 | 77 '-y+1/2, -z+1/2, x'
126 | 78 '-x+1/2, -y+1/2, z'
127 | 79 '-z+1/2, y+1/2, -x'
128 | 80 '-y+1/2, x+1/2, -z'
129 | 81 '-x+1/2, z+1/2, -y'
130 | 82 '-z+1/2, x+1/2, -y'
131 | 83 '-y+1/2, z+1/2, -x'
132 | 84 '-x+1/2, y+1/2, -z'
133 | 85 'z+1/2, -y+1/2, -x'
134 | 86 'y+1/2, -x+1/2, -z'
135 | 87 'x+1/2, -z+1/2, -y'
136 | 88 'z+1/2, -x+1/2, -y'
137 | 89 'y+1/2, -z+1/2, -x'
138 | 90 'x+1/2, -y+1/2, -z'
139 | 91 'z+1/2, y+1/2, x'
140 | 92 'y+1/2, x+1/2, z'
141 | 93 'x+1/2, z+1/2, y'
142 | 94 'z+1/2, x+1/2, y'
143 | 95 'y+1/2, z+1/2, x'
144 | 96 'x+1/2, y+1/2, z'
145 | loop_
146 | _atom_type_symbol
147 | _atom_type_oxidation_number
148 | Li1+ 1
149 | Cl1- -1
150 | S2- -2
151 | P5+ 5
152 | S2- -2
153 | loop_
154 | _atom_site_label
155 | _atom_site_type_symbol
156 | _atom_site_symmetry_multiplicity
157 | _atom_site_Wyckoff_symbol
158 | _atom_site_fract_x
159 | _atom_site_fract_y
160 | _atom_site_fract_z
161 | _atom_site_B_iso_or_equiv
162 | _atom_site_occupancy
163 | _atom_site_attached_hydrogens
164 | Li1 Li1+ 48 h 0.3148(19) 0.018(4) 0.6852(19) 0.104(14) 0.56(6) 0
165 | Cl1 Cl1- 4 a 0. 0. 1. 0.0402(9) 1 0
166 | S1 S2- 4 d 0.25 0.25 0.75 0.0303(8) 1 0
167 | P1 P5+ 4 b 0. 0. 0.5 0.0273(9) 1 0
168 | S2 S2- 16 e 0.11947(13) -0.11947(13) 0.61947(13) 0.0421(7) 1 0
169 | loop_
170 | _atom_site_aniso_label
171 | _atom_site_aniso_type_symbol
172 | _atom_site_aniso_U_11
173 | _atom_site_aniso_U_22
174 | _atom_site_aniso_U_33
175 | _atom_site_aniso_U_12
176 | _atom_site_aniso_U_13
177 | _atom_site_aniso_U_23
178 | Cl1 Cl1- 0.0402(9) 0.0402(9) 0.0402(9) 0. 0. 0.
179 | S1 S2- 0.0303(8) 0.0303(8) 0.0303(8) 0. 0. 0.
180 | P1 P5+ 0.0273(9) 0.0273(9) 0.0273(9) 0. 0. 0.
181 | S2 S2- 0.0421(7) 0.0421(7) 0.0421(7) 0.0080(5) -0.0080(5) 0.0080(5)
182 | #End of TTdata_418490-ICSD


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