{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Compare and plot experimental band gaps from Citrine and computed band gaps from MP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is an example for using the CitrineDataRetrieval to compare experimental band gaps from Citrination (https://citrination.com/) to computed band gaps from the Materials Project (https://www.materialsproject.org/).\n", "\n", "The crystal structure associated with a band gap energy on Citrine is not always available. So, we compare the experimental band gap to the band gap of the MaterialsProject entry with the lowest energy at the same composition.\n", "\n", "This notebook was last updated 11/15/18 for version 0.4.5 of matminer.\n", "\n", "**You will need a Materials Project and Citrine API key, and to start Jupyter notebook with a higher data rate limited (e.g., `jupyter notebook --NotebookApp.iopub_data_rate_limit=1.0e10`)**\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import libraries, and set pandas options to display all rows and columns" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "# Set pandas view options\n", "pd.set_option('display.width', 1000)\n", "pd.set_option('display.max_columns', None)\n", "pd.set_option('display.max_rows', None)\n", "\n", "# filter warnings messages from the notebook\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import matminer's Citrine data retrieval tool, and retrieve 100 experimental band gaps from Citrine's database in a Pandas dataframe. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:02<00:00, 39.24it/s]\n", "100%|██████████| 100/100 [00:04<00:00, 20.72it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "all available fields:\n", "['Thermoluminescence', 'references', 'Crystallinity', 'Lasing-conditions', 'Lasing-dataType', 'Band gap-dataType', 'Thermoluminescence-conditions', 'chemicalFormula', 'Temperature derivative of band gap-units', 'Photoluminescence-conditions', 'Temperature derivative of band gap-conditions', 'category', 'Electroluminescence', 'Morphology', 'uid', 'Temperature derivative of band gap', 'Color', 'Cathodoluminescence', 'Electroluminescence-conditions', 'Temperature derivative of band gap-methods', 'Band gap', 'Cathodoluminescence-conditions', 'Color-dataType', 'Color-conditions', 'Phase_1', 'Phase_2', 'Photoluminescence-dataType', 'Lasing', 'Thermoluminescence-dataType', 'Photoluminescence', 'Band gap-methods', 'Band gap-units', 'Band gap-conditions', 'Temperature derivative of band gap-dataType', 'Phase', 'Cathodoluminescence-dataType', 'Electroluminescence-dataType']\n", "\n", "suggested common fields:\n", "['Thermoluminescence', 'references', 'Crystallinity', 'Lasing-conditions', 'Lasing-dataType', 'Band gap-dataType', 'Thermoluminescence-conditions', 'chemicalFormula', 'Temperature derivative of band gap-units', 'Photoluminescence-conditions', 'Temperature derivative of band gap-conditions', 'Electroluminescence', 'Morphology', 'Temperature derivative of band gap', 'Color', 'Electroluminescence-conditions', 'Temperature derivative of band gap-methods', 'Band gap', 'Color-dataType', 'Color-conditions', 'Photoluminescence-dataType', 'Lasing', 'Thermoluminescence-dataType', 'Photoluminescence', 'Band gap-methods', 'Band gap-units', 'Band gap-conditions', 'Temperature derivative of band gap-dataType', 'Phase', 'Electroluminescence-dataType']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from matminer.data_retrieval.retrieve_Citrine import CitrineDataRetrieval\n", "\n", "api_key = None # Set your Citrine API key here. If set as an environment variable 'CITRINE_KEY', set it to 'None'\n", "c = CitrineDataRetrieval() # Create an adapter to the Citrine Database.\n", "\n", "df = c.get_dataframe(criteria={'data_type': 'EXPERIMENTAL', 'max_results': 100},\n", " properties=['Band gap', 'Temperature'],\n", " common_fields=['chemicalFormula'])\n", "df.rename(columns={'Band gap': 'Experimental band gap'}, inplace=True) # Rename column" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Experimental band gap Band gap-conditions Band gap-dataType Band gap-methods Band gap-units chemicalFormula Temperature derivative of band gap Temperature derivative of band gap-conditions Temperature derivative of band gap-dataType Temperature derivative of band gap-methods Temperature derivative of band gap-units\n", "0 0.153 [{'name': 'Temperature', 'scalars': [{'value':... EXPERIMENTAL [{'name': 'Thermal activation'}] eV Bi2Te3 NaN NaN NaN NaN NaN\n", "1 0.567 [{'name': 'Transition', 'scalars': [{'value': ... EXPERIMENTAL [{'name': 'Absorption'}] eV Mg2Ge1 -0.00018 [{'name': 'Transition', 'scalars': [{'value': ... EXPERIMENTAL [{'name': 'Absorption'}] eV/K\n", "2 0.045 [{'name': 'Temperature', 'scalars': [{'value':... EXPERIMENTAL NaN eV Co1Si1 NaN NaN NaN NaN NaN\n", "3 7.025 [{'name': 'Transition', 'scalars': [{'value': ... EXPERIMENTAL [{'name': 'Reflection'}] eV Na1Br1 NaN NaN NaN NaN NaN\n", "4 0.9 [{'name': 'Temperature', 'scalars': [{'value':... EXPERIMENTAL [{'name': 'Thermal activation'}] eV Ca2Sn1 NaN NaN NaN NaN NaN" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### For each composition, get computed band gap from MP for the most stable structure of that composition" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6.32 s, sys: 595 ms, total: 6.91 s\n", "Wall time: 1min 45s\n" ] } ], "source": [ "%%time\n", "from pymatgen import MPRester, Composition\n", "mpr = MPRester() # provide your API key here or add it to pymatgen\n", "\n", "def get_MP_bandgap(formula):\n", " \"\"\"Given a composition, get the band gap energy of the ground-state structure\n", " at that composition\n", " \n", " Args:\n", " composition (string) - Chemical formula\n", " Returns:\n", " (float) Band gap energy of the ground state structure\"\"\"\n", " # The MPRester requires integer formuals as input\n", " reduced_formula = Composition(formula).get_integer_formula_and_factor()[0]\n", " struct_lst = mpr.get_data(reduced_formula)\n", " \n", " # If there is a structure at this composition, return the band gap energy\n", " if struct_lst:\n", " return sorted(struct_lst, key=lambda e: e['energy_per_atom'])[0]['band_gap']\n", " \n", "df['Computed band gap'] = df['chemicalFormula'].apply(get_MP_bandgap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use FigRecipes to plot experimental vs computed band gaps" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hoverinfo": "x+y+text", "hoverlabel": { "font": { "family": "Courier", "size": 20 } }, "marker": { "colorscale": "Viridis", 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"Ga1Sb1", "Ge1S1", "As2S3", "Ga1Se1", "Hg1Te1", "Mo1O3", "Bi2O3", "Te", "Zn1S1", "As2S3", "V2O5", "Hg1I2", "Pb1Se1", "Mo1Te2", "Mg2Si1", "Ge1S1", "Pb1Se1", "Eu1Se1", "Hg1I2", "Mg2Sn1", "Pb1O1", "Cd3As2", "Bi1I3", "Sb1Si1", "V2O5", "Ga2Te3", "Hg1I2", "Sn1Te1", "Zn3As2", "Sb2S3", "Ga1N1", "Sn1O2", "As2Se3", "Ga1P1", "Al1Sb1", "Zn1S1", "Te", "Bi2Te3", "Sn1S2", "Pb1Se1", "Sb1I3", "Ga2S3", "Pb1Se1", "Cd1Sc1", "In1Se1", "Ga2Te3", "Al1As1", "Ga1P1", "Zn1P2", "Ga1P1", "Mg2Ge1", "Cd1P2", "In2S3", "C", "In2Te3", "Sb2Se3", "Hg1I2", "As2S6", "P", "Eu1S1", "Hg1S1", "Zr1S2", "Mo1O3", "Al2O3", "Sn", "Zn1As2", "Bi1I3", "Cu2Te1", "Hf1Se2", "Se", "Ga1Sb1", "W1O3", "W1Se2", "Cd1Se1", "Bi2Te3", "Ga1Te1", "Bi2O3", "Cd1S1", "Mg2Sn1", "C", "Sn1Te1", "Hg1I2", "Cd1P2", "Zn1P2" ], "type": "scatter", "uid": "c018f1f4-e932-11e8-9498-4a000437e440", "x": [ 0, 10 ], "y": [ 0, 10 ] } ], "layout": { "hoverlabel": { "font": { "family": "Courier", "size": 20 } }, "hovermode": "closest", "legend": { "font": { "family": "Courier", "size": 20 } }, "margin": { "b": 135, "l": 135, "pad": 0, "r": 100, "t": 100 }, "paper_bgcolor": "white", "plot_bgcolor": "white", "title": "", "titlefont": { "family": "Courier", "size": 20 }, "xaxis": { "tickfont": { "family": "Courier", "size": 20 }, "title": "Experimental band gap (eV)", "titlefont": { "family": "Courier", "size": 20 }, "type": "linear" }, "yaxis": { "tickfont": { "family": "Courier", "size": 20 }, "title": "Computed band gap (ev)", "titlefont": { "family": "Courier", "size": 20 }, "type": "linear" } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matminer.figrecipes.plot import PlotlyFig\n", "\n", "pf = PlotlyFig(df, x_title='Experimental band gap (eV)', \n", " y_title='Computed band gap (ev)',mode='notebook', \n", " fontsize=20, ticksize=15)\n", "pf.xy([('Experimental band gap', 'Computed band gap'), ([0, 10], [0, 10])], \n", " modes=['markers', 'lines'], lines=[{}, {'color': 'black', 'dash': 'dash'}],\n", " labels='chemicalFormula', showlegends=False)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Experimental band gapBand gap-conditionsBand gap-dataTypeBand gap-methodsBand gap-unitschemicalFormulaTemperature derivative of band gapTemperature derivative of band gap-conditionsTemperature derivative of band gap-dataTypeTemperature derivative of band gap-methodsTemperature derivative of band gap-unitsComputed band gap
00.153[{'name': 'Temperature', 'scalars': [{'value':...EXPERIMENTAL[{'name': 'Thermal activation'}]eVBi2Te3NaNNaNNaNNaNNaN0.5336
10.567[{'name': 'Transition', 'scalars': [{'value': ...EXPERIMENTAL[{'name': 'Absorption'}]eVMg2Ge1-0.00018[{'name': 'Transition', 'scalars': [{'value': ...EXPERIMENTAL[{'name': 'Absorption'}]eV/K0.1689
20.045[{'name': 'Temperature', 'scalars': [{'value':...EXPERIMENTALNaNeVCo1Si1NaNNaNNaNNaNNaN0.0000
37.025[{'name': 'Transition', 'scalars': [{'value': ...EXPERIMENTAL[{'name': 'Reflection'}]eVNa1Br1NaNNaNNaNNaNNaN4.0899
40.9[{'name': 'Temperature', 'scalars': [{'value':...EXPERIMENTAL[{'name': 'Thermal activation'}]eVCa2Sn1NaNNaNNaNNaNNaN0.0564
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" ], "text/plain": [ " Experimental band gap Band gap-conditions Band gap-dataType Band gap-methods Band gap-units chemicalFormula Temperature derivative of band gap Temperature derivative of band gap-conditions Temperature derivative of band gap-dataType Temperature derivative of band gap-methods Temperature derivative of band gap-units Computed band gap\n", "0 0.153 [{'name': 'Temperature', 'scalars': [{'value':... EXPERIMENTAL [{'name': 'Thermal activation'}] eV Bi2Te3 NaN NaN NaN NaN NaN 0.5336\n", "1 0.567 [{'name': 'Transition', 'scalars': [{'value': ... EXPERIMENTAL [{'name': 'Absorption'}] eV Mg2Ge1 -0.00018 [{'name': 'Transition', 'scalars': [{'value': ... EXPERIMENTAL [{'name': 'Absorption'}] eV/K 0.1689\n", "2 0.045 [{'name': 'Temperature', 'scalars': [{'value':... EXPERIMENTAL NaN eV Co1Si1 NaN NaN NaN NaN NaN 0.0000\n", "3 7.025 [{'name': 'Transition', 'scalars': [{'value': ... EXPERIMENTAL [{'name': 'Reflection'}] eV Na1Br1 NaN NaN NaN NaN NaN 4.0899\n", "4 0.9 [{'name': 'Temperature', 'scalars': [{'value':... EXPERIMENTAL [{'name': 'Thermal activation'}] eV Ca2Sn1 NaN NaN NaN NaN NaN 0.0564" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "matminer python", "language": "python", "name": "matminer" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 1 }