From 45ea3ce9e8f554a6337b03f079954aa9b3b4ece4 Mon Sep 17 00:00:00 2001 From: ncfrey Date: Tue, 12 May 2020 17:52:52 -0400 Subject: [PATCH] Initial commit --- data/X_test.json | 1 + data/X_train.json | 1 + data/mtm_X_test.json | 1 + data/mtm_X_train.json | 1 + data/mtm_y_test.json | 1 + data/mtm_y_train.json | 1 + data/y_test.json | 1 + data/y_train.json | 1 + models.ipynb | 368 +++++++++++++++++++++++++++++++++++++ models/mag_clf.joblib | Bin 0 -> 61333800 bytes models/mag_topo_clf.joblib | Bin 0 -> 9216404 bytes requirements.txt | 5 + 12 files changed, 381 insertions(+) create mode 100644 data/X_test.json create mode 100644 data/X_train.json create mode 100644 data/mtm_X_test.json create mode 100644 data/mtm_X_train.json create mode 100644 data/mtm_y_test.json create mode 100644 data/mtm_y_train.json create mode 100644 data/y_test.json create mode 100644 data/y_train.json create mode 100644 models.ipynb create mode 100644 models/mag_clf.joblib create mode 100644 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]} \ No newline at end of file diff --git a/models.ipynb b/models.ipynb new file mode 100644 index 0000000..584cd64 --- /dev/null +++ b/models.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine learning models for magnetic and topological property prediction in transition metal oxides.\n", + "* Build a classifier that predicts FM vs AFM ground states from structure and composition features.\n", + "* Build a classifier (using structure and composition features) that predicts if a magnetic material has nontrivial band topology for some value of Hubbard $U$.\n", + "\n", + "This notebook accompanies (Frey *et al*., High-throughput search for magnetic and topological order in transition metal oxides)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "__author__ = \"Nathan C. Frey\"\n", + "__copyright__ = \"MIT License\"\n", + "__version__ = \"0.0.1\"\n", + "__maintainer__ = \"Nathan C. Frey\"\n", + "__email__ = \"ncfrey@lbl.gov\"\n", + "__date__ = \"May 5 2020\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Check requirements.txt for version requirements\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.metrics import f1_score, classification_report\n", + "\n", + "from joblib import dump, load\n", + "\n", + "from monty.serialization import loadfn, dumpfn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Machine learning model for predicting magnetic ground state ordering" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "datadir = \"data/\"\n", + "modeldir = \"models/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = pd.read_json(datadir + 'X_train.json')\n", + "X_train = X_train.values\n", + "y_train = loadfn(datadir + 'y_train.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Most important features to consider are values and averages of:\n", + "* Number of magnetic sublattices \n", + "* Sine Coulomb Matrix \n", + "* Nearest-neighbor and next-nearest-neighbor distances between magnetic ions \n", + "* Number of unfilled $d$ orbitals\n", + "* Coordination number of magnetic ions \n", + "* Structural complexity per atom\n", + "* Spacegroup number" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "clf = load(modeldir + 'mag_clf.joblib')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[fold 0] FM score: 0.854\n", + "[fold 0] AFM score: 0.845\n", + "[fold 1] FM score: 0.872\n", + "[fold 1] AFM score: 0.850\n", + "[fold 2] FM score: 0.843\n", + "[fold 2] AFM score: 0.841\n", + "[fold 3] FM score: 0.857\n", + "[fold 3] AFM score: 0.855\n", + "[fold 4] FM score: 0.831\n", + "[fold 4] AFM score: 0.833\n", + "FM Mean: 0.85 median: 0.85 stdev: 0.01\n", + "AFM Mean: 0.84 median: 0.84 stdev: 0.01\n" + ] + } + ], + "source": [ + "fm_scores = []\n", + "afm_scores = []\n", + "\n", + "kf = KFold(n_splits=5, shuffle=True)\n", + "kf.get_n_splits(X_train, y_train)\n", + "\n", + "for k, (train, test) in enumerate(kf.split(X_train, y_train)):\n", + " clf.fit(X_train[train], y_train[train])\n", + " f1s_fm = f1_score(y_train[test], clf.predict(X_train[test]), pos_label=1)\n", + " f1s_afm = f1_score(y_train[test], clf.predict(X_train[test]), pos_label=0)\n", + " fm_scores.append(f1s_fm)\n", + " afm_scores.append(f1s_afm)\n", + " \n", + " print(\"[fold {0}] FM score: {1:.3f}\".format(k, f1s_fm))\n", + " print(\"[fold {0}] AFM score: {1:.3f}\".format(k, f1s_afm))\n", + "\n", + "fm_scores = np.array(fm_scores)\n", + "afm_scores = np.array(afm_scores)\n", + "print('FM Mean: %.2f median: %.2f stdev: %.2f' % (np.mean(fm_scores), np.median(fm_scores), np.std(fm_scores)))\n", + "print('AFM Mean: %.2f median: %.2f stdev: %.2f' % (np.mean(afm_scores), np.median(afm_scores), np.std(afm_scores)))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "X_test = pd.read_json(datadir + 'X_test.json')\n", + "X_test = X_test.values\n", + "y_test = loadfn(datadir + 'y_test.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(clf.score(X_test, y_test), 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " AFM 0.88 0.84 0.86 236\n", + " FM 0.59 0.67 0.63 79\n", + "\n", + " accuracy 0.80 315\n", + " macro avg 0.74 0.76 0.75 315\n", + "weighted avg 0.81 0.80 0.80 315\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf.predict(X_test), target_names=[\"AFM\", \"FM\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Machine learning model for magnetic topological phase classification\n", + "MAGNDATA and appropriate citations available from:\n", + "* https://arxiv.org/ftp/arxiv/papers/2003/2003.00012.pdf and\n", + "* https://www.topologicalquantumchemistry.fr/magnetic/index.html\n", + "* http://webbdcrista1.ehu.es/magndata/" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = pd.read_json(datadir + 'mtm_X_train.json')\n", + "X_train = X_train.values\n", + "y_train = loadfn(datadir + 'mtm_y_train.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Most important features to consider are values and averages of symmetry- and orbital-related descriptors. Some examples are:\n", + "* Number of $d$ valence electrons\n", + "* Spacegroup number\n", + "* Number of unfilled $f$ orbitals\n", + "* Crystal system" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "clf = load(modeldir + 'mag_topo_clf.joblib')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[fold 0] Topological score: 0.683\n", + "[fold 0] Trivial score: 0.711\n", + "[fold 1] Topological score: 0.725\n", + "[fold 1] Trivial score: 0.816\n", + "[fold 2] Topological score: 0.691\n", + "[fold 2] Trivial score: 0.725\n", + "[fold 3] Topological score: 0.791\n", + "[fold 3] Trivial score: 0.759\n", + "[fold 4] Topological score: 0.828\n", + "[fold 4] Trivial score: 0.819\n", + "Topological Mean: 0.74 median: 0.72 stdev: 0.06\n", + "Trivial Mean: 0.77 median: 0.76 stdev: 0.04\n" + ] + } + ], + "source": [ + "topo_scores = []\n", + "triv_scores = []\n", + "\n", + "kf = KFold(n_splits=5, shuffle=True)\n", + "kf.get_n_splits(X_train, y_train)\n", + "\n", + "for k, (train, test) in enumerate(kf.split(X_train, y_train)):\n", + " clf.fit(X_train[train], y_train[train])\n", + " f1s_topo = f1_score(y_train[test], clf.predict(X_train[test]), pos_label=1)\n", + " f1s_triv = f1_score(y_train[test], clf.predict(X_train[test]), pos_label=0)\n", + " topo_scores.append(f1s_topo)\n", + " triv_scores.append(f1s_triv)\n", + " \n", + " print(\"[fold {0}] Topological score: {1:.3f}\".format(k, f1s_topo))\n", + " print(\"[fold {0}] Trivial score: {1:.3f}\".format(k, f1s_triv))\n", + "\n", + "topo_scores = np.array(topo_scores)\n", + "triv_scores = np.array(triv_scores)\n", + "print('Topological Mean: %.2f median: %.2f stdev: %.2f' % (np.mean(topo_scores), np.median(topo_scores), np.std(topo_scores)))\n", + "print('Trivial Mean: %.2f median: %.2f stdev: %.2f' % (np.mean(triv_scores), np.median(triv_scores), np.std(triv_scores)))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "X_test = pd.read_json(datadir + 'mtm_X_test.json')\n", + "X_test = X_test.values\n", + "y_test = loadfn(datadir + 'mtm_y_test.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.72" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(clf.score(X_test, y_test), 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " Trivial 0.87 0.71 0.78 28\n", + " Topological 0.50 0.73 0.59 11\n", + "\n", + " accuracy 0.72 39\n", + " macro avg 0.68 0.72 0.69 39\n", + "weighted avg 0.77 0.72 0.73 39\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf.predict(X_test), target_names=[\"Trivial\", \"Topological\"]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "FastAIEnv", + "language": "python", + "name": "fastai_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/models/mag_clf.joblib b/models/mag_clf.joblib new file mode 100644 index 0000000000000000000000000000000000000000..f0cf2bc8e09b3039b8e7731e8fa468e6c3e665f2 GIT binary patch literal 61333800 zcmdRXcYKt^^Z!E;LO&otK!h_QMnnj`^9)5gA)y8Zl7PWODY+0VD0&u(q7a$_wul`S zqoN`zM#YXAiqc82E0(ihMfvUB^PVTOPd>bU>*?p~$NS^nw>vXCv$M0a`_v0nnlk&M z>DgJiGuvd(oSi*m^7QOB8B=HFX3x&6J~C_O`LkvWh!*-y&ze1Z+SF;;xz&T#nO6rZ 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