add melting points notebook

mpcontribs-portal/notebooks/contribs.materialsproject.org/melting_points.ipynb

@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "respected-disaster",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "from mpcontribs.client import Client\n",
+    "from monty.serialization import loadfn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "changed-vermont",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# client = Client()\n",
+    "# client.create_project(\n",
+    "#     name=\"melting_points\",\n",
+    "#     title=\"Melting Points using GNN model\",\n",
+    "#     authors=\"Q.-J. Hong, S.V. Ushakov, A. van de Walle, A. Navrotsky, M. McDermott\",\n",
+    "#     description=\"\"\"\n",
+    "#     The melting point is a fundamental property that is time-consuming to measure or compute, thus hindering\n",
+    "#     high-throughput analyses of melting relations and phase diagrams over large sets of candidate compounds.\n",
+    "#     To address this, we build a machine learning model, trained on a database of ∼10,000 compounds, that can\n",
+    "#     predict the melting temperature in a fraction of a second. The model, made publicly available online,\n",
+    "#     features graph neural network and residual neural network architectures. We demonstrate the model’s usefulness\n",
+    "#     in diverse applications. For the purpose of materials design and discovery, we show that it can quickly discover\n",
+    "#     novel multicomponent materials with high melting points. These predictions are confirmed by density functional\n",
+    "#     theory calculations and experimentally validated. In an application to planetary science and geology, we employ\n",
+    "#     the model to analyze the melting temperatures of ∼4,800 minerals to uncover correlations relevant to the study of\n",
+    "#     mineral evolution.\n",
+    "#     \"\"\",\n",
+    "#     url=\"https://doi.org/10.1073/pnas.2209630119\",\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "unexpected-edward",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = Client(project=\"melting_points\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "entertaining-variance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "indir = \"/Users/patrick/GoogleDriveLBNL/My Drive/MaterialsProject/gitrepos/mpcontribs-data\"\n",
+    "melting_pts = pd.DataFrame(loadfn(f\"{indir}/melting_points_df_08_08_23.json.gz\"))   # Note: temps in Kelvin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "convenient-myanmar",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "melting_pts.reset_index(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "valid-content",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = melting_pts.to_dict(orient=\"records\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "choice-aluminum",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = {\"MeltingPoint\": \"K\"}\n",
+    "client.init_columns(columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "suspended-mailing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "contributions = []\n",
+    "\n",
+    "for d in data:\n",
+    "    val, err = d[\"melting_point\"], d[\"melting_point_uncertainty\"]\n",
+    "    contributions.append({\n",
+    "        \"identifier\": d[\"index\"],\n",
+    "        \"formula\": d[\"reduced_formula\"],\n",
+    "        \"data\": {\n",
+    "            \"MeltingPoint\": f\"{val}+/-{err} K\"\n",
+    "        }\n",
+    "    })\n",
+    "    \n",
+    "contributions[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffaae86d-f27f-4043-9937-4762f3647794",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(contributions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "surprising-variance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# client.delete_contributions()\n",
+    "client.init_columns(columns)\n",
+    "client.submit_contributions(contributions)\n",
+    "client.init_columns(columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hearing-vaccine",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download = client.download_contributions()"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}