diff --git a/other/jarvis/run_job_using_material_from_jarvis_db.ipynb b/other/jarvis/run_job_using_material_from_jarvis_db.ipynb new file mode 100644 index 00000000..467d0f9c --- /dev/null +++ b/other/jarvis/run_job_using_material_from_jarvis_db.ipynb @@ -0,0 +1,1239 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "\n", + "\"Open\n", + "" + ], + "metadata": { + "collapsed": false, + "id": "3c567b6400249971" + }, + "id": "3c567b6400249971" + }, + { + "cell_type": "markdown", + "source": [ + "## Install Packages\n", + "First, install `express-py` which includes `jarvis-tools` as dependency." + ], + "metadata": { + "collapsed": false, + "id": "8b00ab6854f2263b" + }, + "id": "8b00ab6854f2263b" + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting express-py==2024.1.25.post7\n", + " Downloading express_py-2024.1.25.post7-py3-none-any.whl.metadata (9.1 kB)\n", + "Collecting 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exabyte-json-include, spglib, ruamel.yaml, rdkit-pypi, pybtex, pymatgen, mat3ra-esse, jarvis-tools, ase, express-py\n", + " Attempting uninstall: numpy\n", + " Found existing installation: numpy 1.26.4\n", + " Uninstalling numpy-1.26.4:\n", + " Successfully uninstalled numpy-1.26.4\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "xgboost 2.1.1 requires nvidia-nccl-cu12; platform_system == \"Linux\" and platform_machine != \"aarch64\", which is not installed.\n", + "albucore 0.0.13 requires numpy<2,>=1.24.4, but you have numpy 1.23.5 which is incompatible.\n", + "albumentations 1.4.12 requires numpy>=1.24.4, but you have numpy 1.23.5 which is incompatible.\n", + "chex 0.1.86 requires numpy>=1.24.1, but you have numpy 1.23.5 which is incompatible.\n", + "pandas-stubs 2.1.4.231227 requires numpy>=1.26.0; python_version < \"3.13\", but you have numpy 1.23.5 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed ase-3.23.0 exabyte-json-include-2023.12.23.post0 express-py-2024.1.25.post7 jarvis-tools-2024.5.10 latexcodec-3.0.0 mat3ra-esse-2024.6.4.post1 monty-2024.7.30 munch-2.5.0 numpy-1.23.5 palettable-3.3.3 pybtex-0.24.0 pymatgen-2023.9.25 rdkit-pypi-2022.9.5 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8 spglib-2.5.0 uncertainties-3.2.2 xmltodict-0.13.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "numpy" + ] + }, + "id": "c347a50fa91f480bbfb607464fbcc7ae" + } + }, + "metadata": {} + } + ], + "source": [ + "!pip install express-py==2024.1.25.post7" + ], + "metadata": { + "id": "b1288bc79ee2c828", + "outputId": "6d4b3123-4223-4ff8-e98f-c6a3129ec31d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "id": "b1288bc79ee2c828" + }, + { + "cell_type": "code", + "source": [ + "pip install intermat" + ], + "metadata": { + "id": "rmuCwrRU2kAY", + "outputId": "ca340a89-d157-4160-9df6-71a8815c79cf", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "rmuCwrRU2kAY", + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting intermat\n", + " Using cached intermat-2024.3.24-py2.py3-none-any.whl.metadata (30 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jarvis.db.jsonutils import dumpjson\n", + "import plotly.graph_objects as go\n", + "\n", + "config = {'calculator_method': 'ewald',\n", + " 'disp_intvl': 0.1,\n", + " 'film_index': '1_1_0',\n", + " 'film_jid': 'JVASP-1002',\n", + " 'substrate_index': '1_1_0',\n", + " 'substrate_jid': 'JVASP-1174'}\n", + "\n", + "dumpjson(data=config,filename='config_example2.json')\n", + "!run_intermat.py --config_file config_example2.json >out2\n", + "\n", + "res=loadjson('intermat_results.json')" + ], + "metadata": { + "id": "P6zHf-Sl3rBi", + "outputId": "fc423ce2-78a9-4514-f4df-fdbcff80261a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "P6zHf-Sl3rBi", + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "100% 40.8M/40.8M [00:00<00:00, 59.2MiB/s]\n", + "/usr/local/lib/python3.10/dist-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/intermat/generate.py:52: RuntimeWarning: invalid value encountered in double_scalars\n", + " strain_x = (\n", + "/usr/local/lib/python3.10/dist-packages/intermat/generate.py:55: RuntimeWarning: invalid value encountered in double_scalars\n", + " strain_y = (\n", + "100% 100/100 [00:20<00:00, 4.99it/s]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.contourf(res['wads'],cmap='plasma')\n", + "plt.axis('off')\n" + ], + "metadata": { + "id": "wtXpDYge3-fB", + "outputId": "f4163b89-e31f-4b0e-81ae-2a7c64eefb60", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + } + }, + "id": "wtXpDYge3-fB", + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(0.0, 9.0, 0.0, 9.0)" + ] + }, + "metadata": {}, + "execution_count": 3 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(res.keys())" + ], + "metadata": { + "id": "XyD6CziN4SjN", + "outputId": "89861441-3e9b-4939-dd57-834486a6a676", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "XyD6CziN4SjN", + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "dict_keys(['systems', 'time_taken', 'wads'])\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from jarvis.core.atoms import Atoms\n", + "\n", + "intf = Atoms.from_dict(res['systems'])\n", + "print (intf)" + ], + "metadata": { + "id": "ugq7lJ804VlP", + "outputId": "3bb8bd09-476b-47c5-ade4-6a076113d699", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "ugq7lJ804VlP", + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "System\n", + "1.0\n", + "-0.0 5.73163 -0.0\n", + "8.10575 -0.0 -0.0\n", + "-0.0 -0.0 -26.34364366645526\n", + "As Ga Si \n", + "12 12 24 \n", + "direct\n", + "0.12500000000000017 0.033333436916792386 0.8511499074085056 bottom\n", + "0.12500000000000017 0.5333333678611532 0.6973034974115482 bottom\n", + "0.12500000000000017 0.033333298805513695 0.5434570874145908 bottom\n", + "0.12500000000000017 0.5333332297498745 0.5434570874145905 bottom\n", + "0.12500000000000017 0.033333298805513695 0.6973034974115482 bottom\n", + "0.12500000000000017 0.5333333678611529 0.8511499074085056 bottom\n", + "0.6249999999999999 -0.21666666666666684 0.6203802924130692 bottom\n", + "0.6249999999999999 0.28333340238897264 0.7742267024100271 bottom\n", + "0.6249999999999999 -0.2166665285553881 0.9280731124069845 bottom\n", + "0.6249999999999999 0.28333340238897264 0.9280731124069845 bottom\n", + "0.6249999999999999 -0.21666666666666684 0.7742267024100271 bottom\n", + "0.6249999999999999 0.2833332642776941 0.6203802924130692 bottom\n", + "0.37499999999999994 0.28333340238897264 0.8511499074085056 bottom\n", + "0.37499999999999994 -0.21666666666666684 0.6973034974115482 bottom\n", + "0.37499999999999994 0.2833332642776941 0.5434570874145908 bottom\n", + "0.37499999999999994 0.7833331952220548 0.5434570874145905 bottom\n", + "0.875 0.033333298805513695 0.7742267024100271 bottom\n", + "0.875 0.5333332297498745 0.6203802924130692 bottom\n", + "0.875 0.033333436916792164 0.9280731124069845 bottom\n", + "0.875 0.5333333678611529 0.7742267024100271 bottom\n", + "0.875 0.033333298805513695 0.6203802924130692 bottom\n", + "0.37499999999999994 -0.21666666666666684 0.8511499074085056 bottom\n", + "0.37499999999999994 0.2833332642776941 0.6973034974115482 bottom\n", + "0.875 0.5333333678611532 0.9280731124069845 bottom\n", + "-0.3750000000000001 0.5416666666666665 0.3010994243760031 top\n", + "-0.3750000000000001 0.0416669207054052 0.4485575215231648 top\n", + "-0.12499999999999999 0.2916662856085588 0.1536413272288415 top\n", + "-0.12499999999999999 -0.20833346035270264 0.3010994243760031 top\n", + "0.12500000000000003 0.2916667936860361 0.2273703758024223 top\n", + "0.12500000000000003 -0.20833346035270264 0.07991227865526063 top\n", + "-0.3750000000000001 0.04166641262792811 0.1536413272288415 top\n", + "0.12500000000000003 -0.20833295227522575 0.374828472949584 top\n", + "0.375 0.04166641262792811 0.07991227865526063 top\n", + "-0.12499999999999999 0.2916667936860361 0.44855752152316475 top\n", + "-0.12499999999999999 -0.20833295227522575 0.44855752152316475 top\n", + "-0.3750000000000001 0.04166692070540543 0.30109942437600307 top\n", + "-0.12499999999999999 -0.20833346035270264 0.1536413272288415 top\n", + "-0.3750000000000001 -0.458332825255856 0.44855752152316475 top\n", + "-0.3750000000000001 -0.4583333333333334 0.1536413272288415 top\n", + "0.375 -0.4583333333333334 0.3748284729495839 top\n", + "0.375 0.04166641262792811 0.2273703758024223 top\n", + "0.375 0.5416661585891899 0.07991227865526063 top\n", + "0.12500000000000003 0.2916667936860363 0.3748284729495839 top\n", + "0.12500000000000003 -0.20833346035270264 0.2273703758024223 top\n", + "0.12500000000000003 0.2916662856085588 0.07991227865526063 top\n", + "0.375 -0.4583333333333334 0.2273703758024223 top\n", + "-0.12499999999999999 0.2916667936860361 0.3010994243760031 top\n", + "0.375 0.0416669207054052 0.3748284729495839 top\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "pos_subs=\"\"\"GaAs\n", + "1.0\n", + "3.509895098013499 -6.584084e-10 2.026439508259945\n", + "1.169965032050413 3.3091606691404545 2.026439508259945\n", + "-1.8622597e-09 -1.3168164e-09 4.052878022970947\n", + "Ga As\n", + "1 1\n", + "Cartesian\n", + "0.0 0.0 0.0\n", + "1.1699675 0.82729 2.02644\n", + "\"\"\"\n", + "\n", + "pos_film=\"\"\"Si2\n", + "1.0\n", + "3.3641499856336465 -2.5027128e-09 1.94229273881412\n", + "1.121382991333525 3.1717517190189715 1.9422927388141193\n", + "-2.5909987e-09 -1.8321133e-09 3.884586486670313\n", + "Si\n", + "2\n", + "Cartesian\n", + "3.92483875 2.77528125 6.7980237500000005\n", + "0.56069125 0.39646875 0.9711462500000001\n", + "\"\"\"\n", + "with open('pos_film','w') as f:\n", + " f.write(pos_film)\n", + "\n", + "with open('pos_subs','w') as f:\n", + " f.write(pos_subs)" + ], + "metadata": { + "id": "SD-aFiTF4n_F" + }, + "id": "SD-aFiTF4n_F", + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from jarvis.db.jsonutils import dumpjson,loadjson\n", + "from jarvis.db.jsonutils import dumpjson\n", + "import plotly.graph_objects as go\n", + "\n", + "config = {'calculator_method': 'ewald',\n", + " 'disp_intvl': 0.1,\n", + " 'film_file_path': 'pos_film',\n", + " 'substrate_file_path': 'pos_subs',\n", + " 'film_index': '1_1_0',\n", + " 'substrate_index': '1_1_0'}\n", + "\n", + "dumpjson(data=config,filename='config_example2.json')\n", + "!run_intermat.py --config_file config_example2.json >out2\n", + "\n", + "res=loadjson('intermat_results.json')" + ], + "metadata": { + "id": "iiZy09iU4n8d", + "outputId": "926105ff-b0d0-4d76-8212-ba7c2c108aee", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "iiZy09iU4n8d", + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/usr/local/lib/python3.10/dist-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/intermat/generate.py:52: RuntimeWarning: invalid value encountered in double_scalars\n", + " strain_x = (\n", + "/usr/local/lib/python3.10/dist-packages/intermat/generate.py:55: RuntimeWarning: invalid value encountered in double_scalars\n", + " strain_y = (\n", + "100% 100/100 [00:32<00:00, 3.05it/s]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.contourf(res['wads'],cmap='plasma')\n", + "plt.axis('off')\n" + ], + "metadata": { + "id": "Gbk5MPBw7o1z", + "outputId": "24d0fafa-12c1-42aa-b180-ecc9f298cf19", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + } + }, + "id": "Gbk5MPBw7o1z", + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(0.0, 9.0, 0.0, 9.0)" + ] + }, + "metadata": {}, + "execution_count": 11 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "from jarvis.core.atoms import Atoms\n", + "\n", + "intf = Atoms.from_dict(res['systems'])\n", + "print (intf)" + ], + "metadata": { + "id": "WZ8LRJbK4n6G", + "outputId": "c9535f8e-d0a5-43e4-a219-8820191e2da5", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "WZ8LRJbK4n6G", + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "System\n", + "1.0\n", + "-0.0 5.73163 -0.0\n", + "8.10575 -0.0 -0.0\n", + "-0.0 -0.0 -26.34364366645526\n", + "As Ga Si \n", + "12 12 24 \n", + "direct\n", + "0.12500000000000017 0.033333436916792386 0.8511499074085056 bottom\n", + "0.12500000000000017 0.5333333678611532 0.6973034974115482 bottom\n", + "0.12500000000000017 0.033333298805513695 0.5434570874145908 bottom\n", + "0.12500000000000017 0.5333332297498745 0.5434570874145905 bottom\n", + "0.12500000000000017 0.033333298805513695 0.6973034974115482 bottom\n", + "0.12500000000000017 0.5333333678611529 0.8511499074085056 bottom\n", + "0.6249999999999999 -0.21666666666666684 0.6203802924130692 bottom\n", + "0.6249999999999999 0.28333340238897264 0.7742267024100271 bottom\n", + "0.6249999999999999 -0.2166665285553881 0.9280731124069845 bottom\n", + "0.6249999999999999 0.28333340238897264 0.9280731124069845 bottom\n", + "0.6249999999999999 -0.21666666666666684 0.7742267024100271 bottom\n", + "0.6249999999999999 0.2833332642776941 0.6203802924130692 bottom\n", + "0.37499999999999994 0.28333340238897264 0.8511499074085056 bottom\n", + "0.37499999999999994 -0.21666666666666684 0.6973034974115482 bottom\n", + "0.37499999999999994 0.2833332642776941 0.5434570874145908 bottom\n", + "0.37499999999999994 0.7833331952220548 0.5434570874145905 bottom\n", + "0.875 0.033333298805513695 0.7742267024100271 bottom\n", + "0.875 0.5333332297498745 0.6203802924130692 bottom\n", + "0.875 0.033333436916792164 0.9280731124069845 bottom\n", + "0.875 0.5333333678611529 0.7742267024100271 bottom\n", + "0.875 0.033333298805513695 0.6203802924130692 bottom\n", + "0.37499999999999994 -0.21666666666666684 0.8511499074085056 bottom\n", + "0.37499999999999994 0.2833332642776941 0.6973034974115482 bottom\n", + "0.875 0.5333333678611532 0.9280731124069845 bottom\n", + "-0.3750000000000001 0.5416666666666665 0.3010994243760031 top\n", + "-0.3750000000000001 0.0416669207054052 0.4485575215231648 top\n", + "-0.12499999999999999 0.2916662856085588 0.1536413272288415 top\n", + "-0.12499999999999999 -0.20833346035270264 0.3010994243760031 top\n", + "0.12500000000000003 0.2916667936860361 0.2273703758024223 top\n", + "0.12500000000000003 -0.20833346035270264 0.07991227865526063 top\n", + "-0.3750000000000001 0.04166641262792811 0.1536413272288415 top\n", + "0.12500000000000003 -0.20833295227522575 0.374828472949584 top\n", + "0.375 0.04166641262792811 0.07991227865526063 top\n", + "-0.12499999999999999 0.2916667936860361 0.44855752152316475 top\n", + "-0.12499999999999999 -0.20833295227522575 0.44855752152316475 top\n", + "-0.3750000000000001 0.04166692070540543 0.30109942437600307 top\n", + "-0.12499999999999999 -0.20833346035270264 0.1536413272288415 top\n", + "-0.3750000000000001 -0.458332825255856 0.44855752152316475 top\n", + "-0.3750000000000001 -0.4583333333333334 0.1536413272288415 top\n", + "0.375 -0.4583333333333334 0.3748284729495839 top\n", + "0.375 0.04166641262792811 0.2273703758024223 top\n", + "0.375 0.5416661585891899 0.07991227865526063 top\n", + "0.12500000000000003 0.2916667936860363 0.3748284729495839 top\n", + "0.12500000000000003 -0.20833346035270264 0.2273703758024223 top\n", + "0.12500000000000003 0.2916662856085588 0.07991227865526063 top\n", + "0.375 -0.4583333333333334 0.2273703758024223 top\n", + "-0.12499999999999999 0.2916667936860361 0.3010994243760031 top\n", + "0.375 0.0416669207054052 0.3748284729495839 top\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Get Materials Data From JARVIS\n", + "Then, let\"s get the dataset containing 2D materials from JARVIS and wrap it into a pandas dataframe." + ], + "metadata": { + "collapsed": false, + "id": "f418c51a7f794f9f" + }, + "id": "f418c51a7f794f9f" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from jarvis.db.figshare import data, get_jid_data\n", + "\n", + "dft_2d = data(\"dft_2d\")\n", + "df = pd.DataFrame(dft_2d)" + ], + "metadata": { + "id": "b1ee775d1476f884" + }, + "id": "b1ee775d1476f884" + }, + { + "cell_type": "markdown", + "source": [ + "## Create a simple heterostructure\n", + "\n", + "We use two material ids and place them one above another." + ], + "metadata": { + "collapsed": false, + "id": "9035ff20f1483b33" + }, + "id": "9035ff20f1483b33" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from jarvis.core.atoms import Atoms\n", + "from jarvis.analysis.interface.zur import make_interface\n", + "\n", + "## Note: JVASP-670 is an entry for MoTe2\n", + "jid1 = \"JVASP-670\"\n", + "jid2 =\"JVASP-664\"\n", + "\n", + "for i in dft_2d:\n", + " if i[\"jid\"] == jid1:\n", + " atoms1 = Atoms.from_dict(i[\"atoms\"])\n", + "for i in dft_2d:\n", + " if i[\"jid\"] == jid2:\n", + " atoms2 = Atoms.from_dict(i[\"atoms\"])\n", + "\n", + "interface_atoms_dict = make_interface(film=atoms1, subs=atoms2)\n", + "heterostructure_atoms = interface_atoms_dict[\"interface\"].center_around_origin()\n", + "\n", + "print (jid1,jid2,atoms1.composition.reduced_formula,atoms2.composition.reduced_formula, heterostructure_atoms)" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "1b3e7d016f30b61" + }, + "id": "1b3e7d016f30b61" + }, + { + "cell_type": "markdown", + "source": [ + "## Extract the film and substrate" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + }, + "id": "4d59e226b39bbf41" + }, + "id": "4d59e226b39bbf41" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Using the fractional coordinate Z level of 0.5 to separate top/bottom\n", + "# See also https://github.com/usnistgov/jarvis/issues/311\n", + "heterostructure_atoms_copy = Atoms.from_dict(heterostructure_atoms.to_dict())\n", + "indices_to_remove = []\n", + "for index, coord in enumerate(heterostructure_atoms_copy.coords):\n", + " print(coord, index)\n", + " if coord[2] < 0.5:\n", + " indices_to_remove.append(index)\n", + "for i, ind in enumerate(indices_to_remove):\n", + " heterostructure_atoms_copy = heterostructure_atoms_copy.remove_site_by_index(ind - i)\n", + " print(\"removed\", ind, heterostructure_atoms_copy.props)\n", + "substrate_atoms = Atoms.from_dict(heterostructure_atoms_copy.to_dict())\n", + "\n", + "heterostructure_atoms_copy = Atoms.from_dict(heterostructure_atoms.to_dict())\n", + "indices_to_remove = []\n", + "for index, coord in enumerate(heterostructure_atoms_copy.coords):\n", + " print(coord, index)\n", + " if coord[2] > 0.5:\n", + " indices_to_remove.append(index)\n", + "for i, ind in enumerate(indices_to_remove):\n", + " heterostructure_atoms_copy = heterostructure_atoms_copy.remove_site_by_index(ind - i)\n", + " print(\"removed\", ind, heterostructure_atoms_copy.props)\n", + "film_atoms = Atoms.from_dict(heterostructure_atoms_copy.to_dict())" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "c60f1cdeca83df6a" + }, + "id": "c60f1cdeca83df6a" + }, + { + "cell_type": "markdown", + "source": [ + "## Extract Structure and Convert to ESSE\n", + "Next, we extract an entry from the JARVIS dataset and convert it into ESSE format ready to be uploaded to Mat3ra.com." + ], + "metadata": { + "collapsed": false, + "id": "15fe5f9de299c935" + }, + "id": "15fe5f9de299c935" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import json\n", + "from express import ExPrESS\n", + "\n", + "def jarvis_atoms_to_esse(atoms):\n", + " # JARVIS Atoms return poscar as representation\n", + " # https://github.com/usnistgov/jarvis/blob/master/jarvis/core/atoms.py#L1333\n", + " poscar = atoms.__repr__()\n", + " kwargs = {\n", + " \"structure_string\": poscar,\n", + " \"cell_type\": \"original\",\n", + " \"structure_format\": \"poscar\"\n", + " }\n", + " handler = ExPrESS(\"structure\", **kwargs)\n", + " material = handler.property(\"material\", **kwargs)\n", + " return material\n", + "\n", + "material_film = jarvis_atoms_to_esse(film_atoms)\n", + "material_substrate = jarvis_atoms_to_esse(substrate_atoms)\n", + "material_heterostructure = jarvis_atoms_to_esse(heterostructure_atoms)\n", + "\n", + "# To preview resulting JSON data\n", + "print(json.dumps(material_heterostructure, indent=4))" + ], + "metadata": { + "id": "c41fb68c6d25fe48" + }, + "id": "c41fb68c6d25fe48" + }, + { + "cell_type": "markdown", + "source": [ + "## Setup the API credentials\n", + "Finally, we can upload the material to Mat3ra.com using the REST API: follow the explanation in another example notebook [here](../../examples/material/create_material.ipynb). One can replace the content of the `CONFIG` variable with the JSON data above." + ], + "metadata": { + "collapsed": false, + "id": "a4a48479c7ea090f" + }, + "id": "a4a48479c7ea090f" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# @title Authorization Form\n", + "ACCOUNT_ID = \"\" # @param {type:\"string\"}\n", + "AUTH_TOKEN = \"\" # @param {type:\"string\"}\n", + "MATERIALS_PROJECT_API_KEY = \"MATERIALS_PROJECT_API_KEY\" # @param {type:\"string\"}\n", + "ORGANIZATION_ID = \"\"\n", + "\n", + "\n", + "import os\n", + "\n", + "if \"COLAB_JUPYTER_IP\" in os.environ:\n", + " os.environ.update(\n", + " dict(\n", + " ACCOUNT_ID=ACCOUNT_ID,\n", + " AUTH_TOKEN=AUTH_TOKEN,\n", + " MATERIALS_PROJECT_API_KEY=MATERIALS_PROJECT_API_KEY,\n", + " ORGANIZATION_ID=ORGANIZATION_ID,\n", + " )\n", + " )\n", + "\n", + " !GIT_BRANCH=\"dev\"; export GIT_BRANCH; curl -s \"https://raw.githubusercontent.com/Exabyte-io/api-examples/${GIT_BRANCH}/scripts/env.sh\" | bash" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "5b2ae68afcacc16" + }, + "id": "5b2ae68afcacc16" + }, + { + "cell_type": "markdown", + "source": [ + "## Initialize the API Endpoints" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + }, + "id": "deac6f9f10d31b19" + }, + "id": "deac6f9f10d31b19" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from utils.settings import ENDPOINT_ARGS, ACCOUNT_ID\n", + "from utils.generic import wait_for_jobs_to_finish, get_property_by_subworkflow_and_unit_indicies, dataframe_to_html, display_JSON\n", + "\n", + "# Relevant functions from the API client\n", + "from exabyte_api_client.endpoints.jobs import JobEndpoints\n", + "from exabyte_api_client.endpoints.projects import ProjectEndpoints\n", + "from exabyte_api_client.endpoints.materials import MaterialEndpoints\n", + "from exabyte_api_client.endpoints.bank_workflows import BankWorkflowEndpoints\n", + "from exabyte_api_client.endpoints.properties import PropertiesEndpoints\n", + "\n", + "job_endpoints = JobEndpoints(*ENDPOINT_ARGS)\n", + "project_endpoints = ProjectEndpoints(*ENDPOINT_ARGS)\n", + "material_endpoints = MaterialEndpoints(*ENDPOINT_ARGS)\n", + "property_endpoints = PropertiesEndpoints(*ENDPOINT_ARGS)\n", + "bank_workflow_endpoints = BankWorkflowEndpoints(*ENDPOINT_ARGS)" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "979e35d5e828f794" + }, + "id": "979e35d5e828f794" + }, + { + "cell_type": "markdown", + "source": [ + "## Create Material in the platform" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + }, + "id": "6ea47ba011947ad5" + }, + "id": "6ea47ba011947ad5" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "material_heterostructure[\"name\"] = f\"Heterostructure - {jid1} with {jid2}\"\n", + "material_heterostructure_in_the_platform = material_endpoints.create(material_heterostructure)\n", + "\n", + "material_film[\"name\"] = f\"Film - {jid1}\"\n", + "material_film_in_the_platform = material_endpoints.create(material_film)\n", + "\n", + "material_substrate[\"name\"] = f\"Substrate - {jid2}\"\n", + "material_substrate_in_the_platform = material_endpoints.create(material_substrate)" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "84b4ae0786b716cc" + }, + "id": "84b4ae0786b716cc" + }, + { + "cell_type": "markdown", + "source": [ + "## Create a workflow if it does not exist yet\n", + "\n", + "Here we utilize the id of the Valence Band bank workflow from \"curators\" account `kXJ9j9piJTddX9FHz` and import it if it does not exist in the account collection." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + }, + "id": "ca5e8436bab6f5bd" + }, + "id": "ca5e8436bab6f5bd" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "BANK_ID = \"kXJ9j9piJTddX9FHz\"\n", + "workflow_in_the_platform = bank_workflow_endpoints.copy(BANK_ID, ACCOUNT_ID)" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "443484aaa75ac2ff" + }, + "id": "443484aaa75ac2ff" + }, + { + "cell_type": "markdown", + "source": [ + "## Create and Run a Job\n", + "\n", + "Create Job config" + ], + "metadata": { + "collapsed": false, + "id": "da418324b250a490" + }, + "id": "da418324b250a490" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "PPN = \"4\"\n", + "QUEUE = \"D\"\n", + "NODES = \"1\"\n", + "TIME_LIMIT = \"01:00:00\"\n", + "# Using MSFT Azure cluster with a bit larger memory bandwidth in \"D\" queue\n", + "CLUSTER = \"master-production-20160630-cluster-007.exabyte.io\"\n", + "\n", + "compute = job_endpoints.get_compute(CLUSTER, PPN, NODES, QUEUE, TIME_LIMIT)\n", + "\n", + "config = {\n", + " \"owner\": {\"_id\": ACCOUNT_ID},\n", + " \"_material\": {\n", + " \"_id\": material_heterostructure_in_the_platform[\"_id\"]\n", + " },\n", + " \"_materials\": [\n", + " {\"_id\": material_heterostructure_in_the_platform[\"_id\"]},\n", + " {\"_id\": material_substrate_in_the_platform[\"_id\"]},\n", + " {\"_id\": material_film_in_the_platform[\"_id\"]},\n", + " ],\n", + " \"workflow\": {\"_id\": workflow_in_the_platform[\"_id\"]},\n", + " \"name\": f\"Valence Band - {material_heterostructure['name']}\",\n", + " \"compute\": compute,\n", + "}" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "b4354c90697ee8de" + }, + "id": "b4354c90697ee8de" + }, + { + "cell_type": "markdown", + "source": [ + "Then create a job" + ], + "metadata": { + "collapsed": false, + "id": "c907fce6c3a3f59e" + }, + "id": "c907fce6c3a3f59e" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "job = job_endpoints.create(config)" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "d6537b79de46978a" + }, + "id": "d6537b79de46978a" + }, + { + "cell_type": "markdown", + "source": [ + "then run it" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + }, + "id": "f4b7b12d8cd5019e" + }, + "id": "f4b7b12d8cd5019e" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "job_endpoints.submit(job[\"_id\"])" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "cc878e6674215ecc" + }, + "id": "cc878e6674215ecc" + }, + { + "cell_type": "markdown", + "source": [ + "and wait for it to finish" + ], + "metadata": { + "collapsed": false, + "id": "2ef0dfa179949972" + }, + "id": "2ef0dfa179949972" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from utils.generic import wait_for_jobs_to_finish, get_property_by_subworkflow_and_unit_indicies\n", + "job_id = job[\"_id\"]\n", + "wait_for_jobs_to_finish(job_endpoints, [job_id])" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "436d477f7245ea02" + }, + "id": "436d477f7245ea02" + }, + { + "cell_type": "markdown", + "source": [ + "Then get job files:" + ], + "metadata": { + "collapsed": false, + "id": "2a2cc84332f6d98b" + }, + "id": "2a2cc84332f6d98b" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "files = job_endpoints.list_files(job_id)\n", + "paths = [file[\"key\"] for file in files]\n", + "for path in paths:\n", + " if \"outdir\" not in path:\n", + " print(path)\n", + "\n", + "for file in files:\n", + " if file[\"name\"] == \"pw_scf.out\":\n", + " output_file_metadata = file\n", + "\n", + "import urllib\n", + "\n", + "server_response = urllib.request.urlopen(output_file_metadata[\"signedUrl\"])\n", + "output_file = server_response.read().decode(encoding=\"UTF-8\")\n", + "\n", + "with open(output_file_metadata[\"name\"], \"w\") as file_descriptor:\n", + " file_descriptor.write(output_file)" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "3199c5b7ecf347ef" + }, + "id": "3199c5b7ecf347ef" + }, + { + "cell_type": "markdown", + "source": [ + "and get job properties using the index of a subworkflow in workflow and the index of unit in subworkflow (0, 0) below" + ], + "metadata": { + "collapsed": false, + "id": "d155c1096b7499f5" + }, + "id": "d155c1096b7499f5" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "pressure = get_property_by_subworkflow_and_unit_indicies(property_endpoints, \"pressure\", job, 0, 0)\n", + "print(pressure[\"data\"][\"value\"])" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "651c91cebd378528" + }, + "id": "651c91cebd378528" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/other/material/import_material_from_jarvis_db_entry.ipynb b/other/material/import_material_from_jarvis_db_entry.ipynb index c62f70c5..c4e8e947 100644 --- a/other/material/import_material_from_jarvis_db_entry.ipynb +++ b/other/material/import_material_from_jarvis_db_entry.ipynb @@ -1,137 +1,749 @@ { - "cells": [ - { - "cell_type": "markdown", - "source": [ - "\n", - "\"Open\n", - "" - ], - "metadata": { - "collapsed": false - }, - "id": "3c567b6400249971" + "cells": [ + { + "cell_type": "markdown", + "source": [ + "\n", + "\"Open\n", + "" + ], + "metadata": { + "collapsed": false, + "id": "3c567b6400249971" + }, + "id": "3c567b6400249971" + }, + { + "cell_type": "markdown", + "source": [ + "## Install Packages\n", + "First, install `express-py` which includes `jarvis-tools` as dependency." + ], + "metadata": { + "collapsed": false, + "id": "8b00ab6854f2263b" + }, + "id": "8b00ab6854f2263b" + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.6/82.6 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m41.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m49.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.3/105.3 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.4/29.4 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This behaviour is the source of the following dependency conflicts.\n", + "chex 0.1.85 requires numpy>=1.24.1, but you have numpy 1.23.5 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install -q express-py==2024.1.25.post7" + ], + "metadata": { + "id": "b1288bc79ee2c828", + "outputId": "5d53f995-2900-4d79-e53e-b84943ac6f5f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "b1288bc79ee2c828" + }, + { + "cell_type": "markdown", + "source": [ + "## Get Materials Data From JARVIS\n", + "Then, let's get the dataset containing 2D materials from JARVIS and wrap it into a pandas dataframe." + ], + "metadata": { + "collapsed": false, + "id": "f418c51a7f794f9f" + }, + "id": "f418c51a7f794f9f" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Obtaining 2D dataset 1.1k ...\n", + "Reference:https://www.nature.com/articles/s41524-020-00440-1\n", + "Other versions:https://doi.org/10.6084/m9.figshare.6815705\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 8.39M/8.39M [00:01<00:00, 7.59MiB/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loading the zipfile...\n", + "Loading completed.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from jarvis.db.figshare import data, get_jid_data\n", + "\n", + "dft_2d=data('dft_2d')\n", + "df = pd.DataFrame(dft_2d)" + ], + "metadata": { + "id": "b1ee775d1476f884", + "outputId": "05bad3c3-99f5-47f8-eca0-d2abe89cab60", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "b1ee775d1476f884" + }, + { + "cell_type": "markdown", + "source": [ + "## Extract Structure and Convert to ESSE\n", + "Next, we extract an entry from the JARVIS dataset and convert it into ESSE format ready to be uploaded to Mat3ra.com." + ], + "metadata": { + "collapsed": false, + "id": "15fe5f9de299c935" + }, + "id": "15fe5f9de299c935" + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{\n", + " \"_id\": \"\",\n", + " \"name\": \"material\",\n", + " \"exabyteId\": \"\",\n", + " \"hash\": \"\",\n", + " \"formula\": \"Te2Mo\",\n", + " \"unitCellFormula\": \"Te2 Mo1\",\n", + " \"lattice\": {\n", + " \"type\": \"HEX\",\n", + " \"a\": 3.562364004499092,\n", + " \"b\": 3.562364004499092,\n", + " \"c\": 35.346645,\n", + " \"alpha\": 90.0,\n", + " \"beta\": 90.0,\n", + " \"gamma\": 120.00001186178349,\n", + " \"units\": {\n", + " \"length\": \"angstrom\",\n", + " \"angle\": \"degree\"\n", + " }\n", + " },\n", + " \"basis\": {\n", + " \"units\": \"crystal\",\n", + " \"elements\": [\n", + " {\n", + " \"id\": 1,\n", + " \"value\": \"Te\"\n", + " },\n", + " {\n", + " \"id\": 2,\n", + " \"value\": \"Te\"\n", + " },\n", + " {\n", + " \"id\": 3,\n", + " \"value\": \"Mo\"\n", + " }\n", + " ],\n", + " \"coordinates\": [\n", + " {\n", + " \"id\": 1,\n", + " \"value\": [\n", + " 0.6666669999999999,\n", + " 0.3333330000000001,\n", + " 0.05727934272677686\n", + " ]\n", + " },\n", + " {\n", + " \"id\": 2,\n", + " \"value\": [\n", + " 0.6666669999999999,\n", + " 0.3333330000000001,\n", + " 0.15980865727548643\n", + " ]\n", + " },\n", + " {\n", + " \"id\": 3,\n", + " \"value\": [\n", + " 0.33333299999999993,\n", + " 0.6666670000000001,\n", + " 0.10854400000113165\n", + " ]\n", + " }\n", + " ]\n", + " },\n", + " \"derivedProperties\": [\n", + " {\n", + " \"name\": \"density\",\n", + " \"units\": \"g/cm^3\",\n", + " \"value\": 1.5009769971314555\n", + " },\n", + " {\n", + " \"name\": \"elemental_ratio\",\n", + " \"value\": 0.6666666666666666,\n", + " \"element\": \"Te\"\n", + " },\n", + " {\n", + " \"name\": \"elemental_ratio\",\n", + " \"value\": 0.3333333333333333,\n", + " \"element\": \"Mo\"\n", + " },\n", + " {\n", + " \"name\": \"p-norm\",\n", + " \"value\": 2,\n", + " \"degree\": 0\n", + " },\n", + " {\n", + " \"name\": \"p-norm\",\n", + " \"value\": 0.7453559924999299,\n", + " \"degree\": 2\n", + " },\n", + " {\n", + " \"name\": \"p-norm\",\n", + " \"value\": 0.6933612743506347,\n", + " \"degree\": 3\n", + " },\n", + " {\n", + " \"name\": \"p-norm\",\n", + " \"value\": 0.6707822056951861,\n", + " \"degree\": 5\n", + " },\n", + " {\n", + " \"name\": \"p-norm\",\n", + " \"value\": 0.667408235036379,\n", + " \"degree\": 7\n", + " },\n", + " {\n", + " \"name\": \"p-norm\",\n", + " \"value\": 0.6667317422407864,\n", + " \"degree\": 10\n", + " },\n", + " {\n", + " \"spaceGroupSymbol\": \"P-6m2\",\n", + " \"tolerance\": {\n", + " \"value\": 0.3,\n", + " \"units\": \"angstrom\"\n", + " },\n", + " \"name\": \"symmetry\"\n", + " },\n", + " {\n", + " \"name\": \"volume\",\n", + " \"units\": \"angstrom^3\",\n", + " \"value\": 388.46810374859973\n", + " }\n", + " ],\n", + " \"creator\": {\n", + " \"_id\": \"\",\n", + " \"cls\": \"User\",\n", + " \"slug\": \"\"\n", + " },\n", + " \"owner\": {\n", + " \"_id\": \"\",\n", + " \"cls\": \"Account\",\n", + " \"slug\": \"\"\n", + " },\n", + " \"schemaVersion\": \"0.2.0\"\n", + "}\n" + ] + } + ], + "source": [ + "import json\n", + "from express import ExPrESS\n", + "\n", + "## Note: JVASP-670 is an entry for MoTe2\n", + "jarvis_db_entry = json.dumps(json.loads(df[(df['jid']=='JVASP-670')].to_json(orient='records'))[0])\n", + "\n", + "kwargs = {\n", + " \"structure_string\": jarvis_db_entry,\n", + " \"cell_type\": \"original\",\n", + " \"structure_format\": \"jarvis-db-entry\"\n", + "}\n", + "\n", + "handler = ExPrESS(\"structure\", **kwargs)\n", + "data = handler.property(\"material\", **kwargs)\n", + "\n", + "# To preview resulting JSON data\n", + "print(json.dumps(data, indent=4))" + ], + "metadata": { + "id": "c41fb68c6d25fe48", + "outputId": "5b4dde68-58de-4301-8ac1-2cf55c1f1e0a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "c41fb68c6d25fe48" + }, + { + "cell_type": "markdown", + "source": [ + "## Upload to Mat3ra.com\n", + "Finally, we can upload the material to Mat3ra.com using the REST API: follow the explanation in another example notebook [here](../../examples/material/create_material.ipynb). One can replace the content of the `CONFIG` variable with the JSON data above." + ], + "metadata": { + "collapsed": false, + "id": "a4a48479c7ea090f" + }, + "id": "a4a48479c7ea090f" + }, + { + "cell_type": "code", + "source": [ + "# @title Authorization Form\n", + "ACCOUNT_ID = \"xxxxxxxxxx\" # @param {type:\"string\"}\n", + "AUTH_TOKEN = \"xxxxxxxxxx\" # @param {type:\"string\"}\n", + "ORGANIZATION_ID = \"\"\n", + "\n", + "\n", + "import os\n", + "\n", + "if \"COLAB_JUPYTER_IP\" in os.environ:\n", + " os.environ.update(\n", + " dict(\n", + " ACCOUNT_ID=ACCOUNT_ID,\n", + " AUTH_TOKEN=AUTH_TOKEN,\n", + " MATERIALS_PROJECT_API_KEY=MATERIALS_PROJECT_API_KEY,\n", + " ORGANIZATION_ID=ORGANIZATION_ID,\n", + " )\n", + " )\n", + "\n", + " !GIT_BRANCH=\"dev\"; export GIT_BRANCH; curl -s \"https://raw.githubusercontent.com/Exabyte-io/api-examples/${GIT_BRANCH}/scripts/env.sh\" | bash\n" + ], + "metadata": { + "id": "jMqKRCE1Df9W", + "outputId": "a3d60149-a1fe-477a-bb68-34a7dcd4906c", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "jMqKRCE1Df9W", + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Traceback (most recent call last):\n", + " File \"/usr/local/bin/notebook-path\", line 8, in \n", + " sys.exit(print_notebook_path())\n", + " File \"/usr/local/lib/python3.10/dist-packages/utils/notebook.py\", line 46, in print_notebook_path\n", + " print(get_notebook_info()[\"notebook_path\"])\n", + " File \"/usr/local/lib/python3.10/dist-packages/utils/notebook.py\", line 22, in get_notebook_info\n", + " parsed = re.findall(\"(.*)/blob/(.*)/examples/(.*)\", path)[0]\n", + "IndexError: list index out of range\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Relevant functions from the API client" + ], + "metadata": { + "id": "UlNC2xpSEaQn" + }, + "id": "UlNC2xpSEaQn" + }, + { + "cell_type": "code", + "source": [ + "from utils.settings import ENDPOINT_ARGS, ACCOUNT_ID\n", + "from utils.generic import wait_for_jobs_to_finish, get_property_by_subworkflow_and_unit_indicies, dataframe_to_html, display_JSON\n", + "\n", + "import pandas as pd\n", + "from exabyte_api_client.endpoints.jobs import JobEndpoints\n", + "from exabyte_api_client.endpoints.projects import ProjectEndpoints\n", + "from exabyte_api_client.endpoints.materials import MaterialEndpoints\n", + "from exabyte_api_client.endpoints.bank_workflows import BankWorkflowEndpoints\n", + "from exabyte_api_client.endpoints.properties import PropertiesEndpoints\n", + "from exabyte_api_client.endpoints.workflows import WorkflowEndpoints\n", + "\n", + "job_endpoints = JobEndpoints(*ENDPOINT_ARGS)\n", + "project_endpoints = ProjectEndpoints(*ENDPOINT_ARGS)\n", + "material_endpoints = MaterialEndpoints(*ENDPOINT_ARGS)\n", + "property_endpoints = PropertiesEndpoints(*ENDPOINT_ARGS)\n", + "bank_workflow_endpoints = BankWorkflowEndpoints(*ENDPOINT_ARGS)\n", + "workflow_endpoints = WorkflowEndpoints(*ENDPOINT_ARGS)" + ], + "metadata": { + "id": "983PpaMPDgAJ" + }, + "id": "983PpaMPDgAJ", + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Make an example heterostructure" + ], + "metadata": { + "id": "efbVRY9JDu_H" + }, + "id": "efbVRY9JDu_H" + }, + { + "cell_type": "code", + "source": [ + "from jarvis.core.atoms import Atoms\n", + "from jarvis.analysis.interface.zur import make_interface\n", + "from jarvis.db.figshare import data\n", + "from jarvis.io.vasp.inputs import Poscar\n", + "import sys\n", + "\n", + "dft_2d_structs = data('dft_2d')\n", + "\n", + "jid1= 'JVASP-670'\n", + "jid2='JVASP-664'\n", + "for i in dft_2d_structs:\n", + " if i['jid']==jid1:\n", + " atoms1 = Atoms.from_dict(i['atoms'])\n", + "for i in dft_2d_structs:\n", + " if i['jid']==jid2:\n", + " atoms2 = Atoms.from_dict(i['atoms'])\n", + "\n", + "interf = make_interface(film=atoms1, subs=atoms2)\n", + "hetero = interf['interface'].center_around_origin()\n", + "print (jid1,jid2,atoms1.composition.reduced_formula,atoms2.composition.reduced_formula, hetero)\n", + "filename='POSCAR-'+jid1+jid2+'.vasp'\n", + "Poscar(hetero).write_file(filename)\n" + ], + "metadata": { + "id": "9xvUwBQfDgCM", + "outputId": "5ca8474b-03f0-420d-effe-92ec5fa8d2b6", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "9xvUwBQfDgCM", + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Obtaining 2D dataset 1.1k ...\n", + "Reference:https://www.nature.com/articles/s41524-020-00440-1\n", + "Other versions:https://doi.org/10.6084/m9.figshare.6815705\n", + "Loading the zipfile...\n", + "Loading completed.\n", + "mismatch_u,mismatch_v -0.03226692878349979 -0.03226692878349979\n", + "JVASP-670 JVASP-664 Te2Mo MoS2 System\n", + "1.0\n", + "6.37593 -0.0 -0.0\n", + "3.18796 -5.52171 -0.0\n", + "-0.0 -0.0 -25.75734579378708\n", + "Mo S Te \n", + "7 8 6 \n", + "direct\n", + "0.36111136029246144 0.02777693088071573 0.6061329711247329 bottom\n", + "0.5555559999999957 0.11111066666667094 0.3584887699946901 bottom\n", + "0.22222266666666238 -0.2222226666666623 0.3584887699946901 bottom\n", + "-0.11111066666667099 -0.5555559999999956 0.3584887699946901 bottom\n", + "0.36111136029246127 -0.4722230691192842 0.6061329711247329 bottom\n", + "-0.13888863970753865 -0.4722230691192842 0.6061329711247329 bottom\n", + "-0.1388886397075386 0.02777693088071573 0.6061329711247329 bottom\n", + "0.19444431985376934 0.36111153455964207 0.5453107527903425 bottom\n", + "-0.30555568014623086 -0.13888846544035782 0.6669565435968711 bottom\n", + "-0.30555568014623075 0.36111153455964207 0.6669565435968711 bottom\n", + "0.19444431985376934 0.36111153455964207 0.6669565435968711 bottom\n", + "-0.30555568014623086 -0.13888846544035782 0.5453107527903425 bottom\n", + "-0.30555568014623075 0.36111153455964207 0.5453107527903425 top\n", + "0.1944443198537693 -0.13888846544035782 0.5453107527903425 top\n", + "0.1944443198537693 -0.13888846544035782 0.6669565435968711 top\n", + "-0.11111133333333112 0.11111133333333116 0.28813862196335355 top\n", + "-0.4444446666666646 -0.2222220000000021 0.28813862196335355 top\n", + "0.22222200000000225 0.44444466666666443 0.42883891802602647 top\n", + "-0.11111133333333112 0.11111133333333116 0.42883891802602647 top\n", + "-0.4444446666666646 -0.2222220000000021 0.42883891802602647 top\n", + "0.22222200000000225 0.44444466666666443 0.28813862196335355 top\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "2OWzUG3ODssG" + }, + "id": "2OWzUG3ODssG", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "interf.keys()" + ], + "metadata": { + "id": "u_4WMY3WDyvr", + "outputId": "64fe059a-94ab-48d7-d511-c08a63d53476", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "u_4WMY3WDyvr", + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "dict_keys(['mismatch_u', 'mismatch_v', 'mismatch_angle', 'area1', 'area2', 'film_sl', 'matches', 'subs_sl', 'interface'])" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from jarvis.core.atoms import Atoms\n", + "a=Atoms.from_poscar(\"POSCAR-JVASP-670JVASP-664.vasp\")" + ], + "metadata": { + "id": "Pj0f3m1LDyyP" + }, + "id": "Pj0f3m1LDyyP", + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import json\n", + "from express import ExPrESS\n", + "from utils.generic import wait_for_jobs_to_finish, get_property_by_subworkflow_and_unit_indicies\n", + "## Note: JVASP-670 is an entry for MoTe2\n", + "\n", + "\n", + "def atoms_to_job(name='interface-670-664', atoms=[],workflow=[]):\n", + " jarvis_db_entry = json.dumps({\"atoms\":atoms.to_dict()})\n", + " #json.dumps(json.loads(df[(df['jid']=='JVASP-670')].to_json(orient='records'))[0])\n", + "\n", + " kwargs = {\n", + " \"structure_string\": jarvis_db_entry,\n", + " \"cell_type\": \"original\",\n", + " \"structure_format\": \"jarvis-db-entry\"\n", + " }\n", + "\n", + " handler = ExPrESS(\"structure\", **kwargs)\n", + " data = handler.property(\"material\", **kwargs)\n", + " data[\"name\"] = name\n", + " material = material_endpoints.create(data)\n", + " default_workflow = workflow_endpoints.list({\"isDefault\": True, \"owner._id\": ACCOUNT_ID})[0]\n", + " workflow_id = default_workflow[\"_id\"]\n", + " owner_id = material[\"owner\"][\"_id\"]\n", + " config = {\n", + " \"owner\": {\"_id\": owner_id},\n", + " \"_material\": {\"_id\": material[\"_id\"]},\n", + " \"workflow\": {\"_id\": workflow_id},\n", + " \"name\": name,\n", + " }\n", + " job = job_endpoints.create(config)\n", + " job_endpoints.submit(job[\"_id\"])\n", + "\n", + " job_id = job[\"_id\"]\n", + " print(\"job_id\",job_id)\n", + " wait_for_jobs_to_finish(job_endpoints, [job_id])\n", + " return job_id" + ], + "metadata": { + "id": "POEKn0RrDy0L" + }, + "id": "POEKn0RrDy0L", + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "atoms_to_job(atoms=a)" + ], + "metadata": { + "id": "xXZ8npX5Dy2i", + "outputId": "347341aa-4614-49f8-c810-09b22fac22a2", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "id": "xXZ8npX5Dy2i", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "job_id evFSF5Zgt4v2ZWeEW\n", + "Wait for jobs to finish, poll interval: 10 sec\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "| TIME | SUBMITTED-JOBS | ACTIVE-JOBS | FINISHED-JOBS | ERRORED-JOBS |\n", + "+=====================+==================+===============+=================+================+\n", + "| 2024-03-02-21:27:20 | 1 | 0 | 0 | 0 |\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "| TIME | SUBMITTED-JOBS | ACTIVE-JOBS | FINISHED-JOBS | ERRORED-JOBS |\n", + "+=====================+==================+===============+=================+================+\n", + "| 2024-03-02-21:27:30 | 1 | 0 | 0 | 0 |\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "| TIME | SUBMITTED-JOBS | ACTIVE-JOBS | FINISHED-JOBS | ERRORED-JOBS |\n", + "+=====================+==================+===============+=================+================+\n", + "| 2024-03-02-21:27:41 | 1 | 0 | 0 | 0 |\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "| TIME | SUBMITTED-JOBS | ACTIVE-JOBS | FINISHED-JOBS | ERRORED-JOBS |\n", + "+=====================+==================+===============+=================+================+\n", + "| 2024-03-02-21:27:51 | 0 | 1 | 0 | 0 |\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "| TIME | SUBMITTED-JOBS | ACTIVE-JOBS | FINISHED-JOBS | ERRORED-JOBS |\n", + "+=====================+==================+===============+=================+================+\n", + "| 2024-03-02-21:28:01 | 0 | 1 | 0 | 0 |\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "| TIME | SUBMITTED-JOBS | ACTIVE-JOBS | FINISHED-JOBS | ERRORED-JOBS |\n", + "+=====================+==================+===============+=================+================+\n", + "| 2024-03-02-21:28:12 | 0 | 1 | 0 | 0 |\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "| TIME | SUBMITTED-JOBS | ACTIVE-JOBS | FINISHED-JOBS | ERRORED-JOBS |\n", + "+=====================+==================+===============+=================+================+\n", + "| 2024-03-02-21:28:22 | 0 | 1 | 0 | 0 |\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "| TIME | SUBMITTED-JOBS | ACTIVE-JOBS | FINISHED-JOBS | ERRORED-JOBS |\n", + "+=====================+==================+===============+=================+================+\n", + "| 2024-03-02-21:28:32 | 0 | 1 | 0 | 0 |\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "| TIME | SUBMITTED-JOBS | ACTIVE-JOBS | FINISHED-JOBS | ERRORED-JOBS |\n", + "+=====================+==================+===============+=================+================+\n", + "| 2024-03-02-21:28:43 | 0 | 1 | 0 | 0 |\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n", + "| TIME | SUBMITTED-JOBS | ACTIVE-JOBS | FINISHED-JOBS | ERRORED-JOBS |\n", + "+=====================+==================+===============+=================+================+\n", + "| 2024-03-02-21:28:53 | 0 | 1 | 0 | 0 |\n", + "+---------------------+------------------+---------------+-----------------+----------------+\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "1MyDBpjLDy49" + }, + "id": "1MyDBpjLDy49", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "5bRINfFtDy7Q" + }, + "id": "5bRINfFtDy7Q", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "0DQvEeiuDy9m" + }, + "id": "0DQvEeiuDy9m", + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + }, + "colab": { + "provenance": [] + } }, - { - "cell_type": "markdown", - "source": [ - "## Install Packages\n", - "First, install `express-py` which includes `jarvis-tools` as dependency." - ], - "metadata": { - "collapsed": false - }, - "id": "8b00ab6854f2263b" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "!pip install express-py==2024.1.25.post7" - ], - "metadata": { - "collapsed": false - }, - "id": "b1288bc79ee2c828" - }, - { - "cell_type": "markdown", - "source": [ - "## Get Materials Data From JARVIS\n", - "Then, let's get the dataset containing 2D materials from JARVIS and wrap it into a pandas dataframe." - ], - "metadata": { - "collapsed": false - }, - "id": "f418c51a7f794f9f" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from jarvis.db.figshare import data, get_jid_data\n", - "\n", - "dft_2d=data('dft_2d')\n", - "df = pd.DataFrame(dft_2d)" - ], - "metadata": { - "collapsed": false - }, - "id": "b1ee775d1476f884" - }, - { - "cell_type": "markdown", - "source": [ - "## Extract Structure and Convert to ESSE\n", - "Next, we extract an entry from the JARVIS dataset and convert it into ESSE format ready to be uploaded to Mat3ra.com." - ], - "metadata": { - "collapsed": false - }, - "id": "15fe5f9de299c935" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "import json\n", - "from express import ExPrESS\n", - "\n", - "## Note: JVASP-670 is an entry for MoTe2\n", - "jarvis_db_entry = json.dumps(json.loads(df[(df['jid']=='JVASP-670')].to_json(orient='records'))[0])\n", - "\n", - "kwargs = {\n", - " \"structure_string\": jarvis_db_entry,\n", - " \"cell_type\": \"original\",\n", - " \"structure_format\": \"jarvis-db-entry\"\n", - "}\n", - "\n", - "handler = ExPrESS(\"structure\", **kwargs)\n", - "data = handler.property(\"material\", **kwargs)\n", - "\n", - "# To preview resulting JSON data\n", - "print(json.dumps(data, indent=4))" - ], - "metadata": { - "collapsed": false - }, - "id": "c41fb68c6d25fe48" - }, - { - "cell_type": "markdown", - "source": [ - "## Upload to Mat3ra.com\n", - "Finally, we can upload the material to Mat3ra.com using the REST API: follow the explanation in another example notebook [here](../../examples/material/create_material.ipynb). One can replace the content of the `CONFIG` variable with the JSON data above." - ], - "metadata": { - "collapsed": false - }, - "id": "a4a48479c7ea090f" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file