├── .gitignore ├── APS 2023 GNN tutorial.pdf ├── LICENSE ├── README.md ├── alignn ├── JARVIS-APS.pdf ├── Training_ALIGNN_model_example.ipynb └── __init__.py └── intro_lecture ├── apsmarch23_gnn_tutorial.ipynb └── intro_to_gnns_apsmarch23.pdf /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /APS 2023 GNN tutorial.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/williamratcliff/GNN-tutorial-APS-March-2023/d7a8b65083bb80595cf14d9404080b3f3e50b286/APS 2023 GNN tutorial.pdf -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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Installing [ALIGNN](https://github.com/usnistgov/alignn)\n", 38 | "2. Example training for regression on 50 materials,\n", 39 | "3. Using pre-trained models to make fast predictions\n", 40 | "4. Using ALIGNN-FF model to predict the unrelaxed energy (fast), optimized strcture and energy, and EV curve\n", 41 | "5. Train ALIGNN-FF on a new dataset\n", 42 | "6. Training [JARVIS-DFT](https://jarvis.nist.gov/jarvisdft) 2D exfoliation energy model \n", 43 | "7. Training [QM9](http://quantum-machine.org/datasets/) U0 model" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "metadata": { 49 | "id": "WFrl_N-S1Bxk", 50 | "outputId": "350ce494-1992-4164-df8b-357925d1f408", 51 | "colab": { 52 | "base_uri": "https://localhost:8080/" 53 | } 54 | }, 55 | "source": [ 56 | "!pip install alignn" 57 | ], 58 | "execution_count": null, 59 | "outputs": [ 60 | { 61 | "output_type": "stream", 62 | "name": "stdout", 63 | "text": [ 64 | "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", 65 | "Collecting alignn\n", 66 | " Downloading alignn-2023.1.10-py2.py3-none-any.whl (15.1 MB)\n", 67 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.1/15.1 MB\u001b[0m \u001b[31m36.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 68 | "\u001b[?25hRequirement already satisfied: scipy>=1.6.1 in /usr/local/lib/python3.8/dist-packages (from alignn) (1.7.3)\n", 69 | "Collecting 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(4.0.0)\n", 140 | "Installing collected packages: xmltodict, torch, spglib, pyparsing, pyflakes, pydocstyle, pycodestyle, psutil, mccabe, fonttools, contourpy, pytorch-ignite, matplotlib, flake8, dgl, jarvis-tools, ase, alignn\n", 141 | " Attempting uninstall: torch\n", 142 | " Found existing installation: torch 1.13.1+cu116\n", 143 | " Uninstalling torch-1.13.1+cu116:\n", 144 | " Successfully uninstalled torch-1.13.1+cu116\n", 145 | " Attempting uninstall: pyparsing\n", 146 | " Found existing installation: pyparsing 3.0.9\n", 147 | " Uninstalling pyparsing-3.0.9:\n", 148 | " Successfully uninstalled pyparsing-3.0.9\n", 149 | " Attempting uninstall: psutil\n", 150 | " Found existing installation: psutil 5.4.8\n", 151 | " Uninstalling psutil-5.4.8:\n", 152 | " Successfully uninstalled psutil-5.4.8\n", 153 | " Attempting uninstall: matplotlib\n", 154 | " Found existing installation: matplotlib 3.2.2\n", 155 | " Uninstalling matplotlib-3.2.2:\n", 156 | " Successfully uninstalled matplotlib-3.2.2\n", 157 | "\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", 158 | "torchvision 0.14.1+cu116 requires torch==1.13.1, but you have torch 1.12.0 which is incompatible.\n", 159 | "torchtext 0.14.1 requires torch==1.13.1, but you have torch 1.12.0 which is incompatible.\n", 160 | "torchaudio 0.13.1+cu116 requires torch==1.13.1, but you have torch 1.12.0 which is incompatible.\u001b[0m\u001b[31m\n", 161 | "\u001b[0mSuccessfully installed alignn-2023.1.10 ase-3.22.1 contourpy-1.0.7 dgl-1.0.0 flake8-6.0.0 fonttools-4.38.0 jarvis-tools-2023.1.8 matplotlib-3.6.3 mccabe-0.7.0 psutil-5.9.4 pycodestyle-2.10.0 pydocstyle-6.3.0 pyflakes-3.0.1 pyparsing-2.4.7 pytorch-ignite-0.5.0.dev20221024 spglib-2.0.2 torch-1.12.0 xmltodict-0.13.0\n" 162 | ] 163 | } 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "metadata": { 169 | "colab": { 170 | "base_uri": "https://localhost:8080/" 171 | }, 172 | "id": "JyyE-cHL2iOn", 173 | "outputId": "a0439df3-23f0-448d-894b-27e731f576e0" 174 | }, 175 | "source": [ 176 | "import os\n", 177 | "!pwd\n", 178 | "os.chdir('/content')\n", 179 | "# Clone ALIGNN repo to get example folder\n", 180 | "if not os.path.exists('alignn'):\n", 181 | " !git clone https://github.com/usnistgov/alignn.git\n", 182 | "\n", 183 | "os.chdir('alignn')\n", 184 | "# Install using setup.py in case pip didn't work\n", 185 | "# !python setup.py develop\n", 186 | "\n", 187 | "#!pip install dgl-cu111 # Colab has cuda 11.1" 188 | ], 189 | "execution_count": null, 190 | "outputs": [ 191 | { 192 | "output_type": "stream", 193 | "name": "stdout", 194 | "text": [ 195 | "/content\n", 196 | "Cloning into 'alignn'...\n", 197 | "remote: Enumerating objects: 3330, done.\u001b[K\n", 198 | "remote: Counting objects: 100% (924/924), done.\u001b[K\n", 199 | "remote: Compressing objects: 100% (256/256), done.\u001b[K\n", 200 | "remote: Total 3330 (delta 711), reused 780 (delta 643), pack-reused 2406\u001b[K\n", 201 | "Receiving objects: 100% (3330/3330), 32.70 MiB | 7.98 MiB/s, done.\n", 202 | "Resolving deltas: 100% (1887/1887), done.\n" 203 | ] 204 | } 205 | ] 206 | }, 207 | { 208 | "cell_type": "markdown", 209 | "metadata": { 210 | "id": "wsJg4A_s2umV" 211 | }, 212 | "source": [ 213 | "Example folder with id_prop.csv and 'POSCAR files.'" 214 | ] 215 | }, 216 | { 217 | "cell_type": "code", 218 | "metadata": { 219 | "id": "cy1tmx3V2uC7", 220 | "colab": { 221 | "base_uri": "https://localhost:8080/" 222 | }, 223 | "outputId": "14a4a5f0-34e8-446b-8856-0acd82680b3f" 224 | }, 225 | "source": [ 226 | "!ls \"alignn/examples/sample_data\"" 227 | ], 228 | "execution_count": null, 229 | "outputs": [ 230 | { 231 | "output_type": "stream", 232 | "name": "stdout", 233 | "text": [ 234 | "config_example.json\t POSCAR-JVASP-64045.vasp POSCAR-JVASP-86097.vasp\n", 235 | "id_prop.csv\t\t POSCAR-JVASP-64240.vasp POSCAR-JVASP-86205.vasp\n", 236 | "POSCAR-JVASP-107772.vasp POSCAR-JVASP-64377.vasp POSCAR-JVASP-86436.vasp\n", 237 | "POSCAR-JVASP-10.vasp\t POSCAR-JVASP-64584.vasp POSCAR-JVASP-86726.vasp\n", 238 | "POSCAR-JVASP-13526.vasp POSCAR-JVASP-64664.vasp POSCAR-JVASP-86968.vasp\n", 239 | "POSCAR-JVASP-1372.vasp\t POSCAR-JVASP-64719.vasp POSCAR-JVASP-89025.vasp\n", 240 | "POSCAR-JVASP-14014.vasp POSCAR-JVASP-64906.vasp POSCAR-JVASP-89265.vasp\n", 241 | "POSCAR-JVASP-14441.vasp POSCAR-JVASP-65062.vasp POSCAR-JVASP-90228.vasp\n", 242 | "POSCAR-JVASP-14873.vasp POSCAR-JVASP-65101.vasp POSCAR-JVASP-90532.vasp\n", 243 | "POSCAR-JVASP-15345.vasp POSCAR-JVASP-655.vasp POSCAR-JVASP-90856.vasp\n", 244 | "POSCAR-JVASP-1996.vasp\t POSCAR-JVASP-676.vasp POSCAR-JVASP-97378.vasp\n", 245 | "POSCAR-JVASP-21210.vasp POSCAR-JVASP-76308.vasp POSCAR-JVASP-97499.vasp\n", 246 | "POSCAR-JVASP-22556.vasp POSCAR-JVASP-76309.vasp POSCAR-JVASP-97570.vasp\n", 247 | "POSCAR-JVASP-27901.vasp POSCAR-JVASP-76312.vasp POSCAR-JVASP-97677.vasp\n", 248 | "POSCAR-JVASP-28397.vasp POSCAR-JVASP-76313.vasp POSCAR-JVASP-97799.vasp\n", 249 | "POSCAR-JVASP-28565.vasp POSCAR-JVASP-76318.vasp POSCAR-JVASP-97915.vasp\n", 250 | "POSCAR-JVASP-28634.vasp POSCAR-JVASP-76515.vasp POSCAR-JVASP-97984.vasp\n", 251 | "POSCAR-JVASP-28704.vasp POSCAR-JVASP-76516.vasp POSCAR-JVASP-98167.vasp\n", 252 | "POSCAR-JVASP-42300.vasp POSCAR-JVASP-76525.vasp POSCAR-JVASP-98224.vasp\n", 253 | "POSCAR-JVASP-48166.vasp POSCAR-JVASP-76528.vasp POSCAR-JVASP-98225.vasp\n", 254 | "POSCAR-JVASP-50332.vasp POSCAR-JVASP-76536.vasp POSCAR-JVASP-98284.vasp\n", 255 | "POSCAR-JVASP-60596.vasp POSCAR-JVASP-76548.vasp POSCAR-JVASP-98550.vasp\n", 256 | "POSCAR-JVASP-60702.vasp POSCAR-JVASP-76549.vasp scripts\n", 257 | "POSCAR-JVASP-63912.vasp POSCAR-JVASP-76562.vasp\n", 258 | "POSCAR-JVASP-64003.vasp POSCAR-JVASP-76567.vasp\n" 259 | ] 260 | } 261 | ] 262 | }, 263 | { 264 | "cell_type": "markdown", 265 | "metadata": { 266 | "id": "jUNiKBBV211E" 267 | }, 268 | "source": [ 269 | "# 50 materials and their bandgap data generated with the script [generate_sample_data_reg.py](https://github.com/usnistgov/alignn/blob/main/alignn/examples/sample_data/scripts/generate_sample_data_reg.py)" 270 | ] 271 | }, 272 | { 273 | "cell_type": "markdown", 274 | "metadata": { 275 | "id": "FbzuGCA332yS" 276 | }, 277 | "source": [ 278 | "# Train a model for 3 epochs and batch size of 2. Other parameters are provided in `config_example.json` file. For an involved training, use higher batch size such as 16 and epochs such as 300." 279 | ] 280 | }, 281 | { 282 | "cell_type": "markdown", 283 | "metadata": { 284 | "id": "HNHla4FDKRre" 285 | }, 286 | "source": [ 287 | "Command line train_folder.py is used below." 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "metadata": { 293 | "id": "l5JkSMwx2cfy", 294 | "colab": { 295 | "base_uri": "https://localhost:8080/" 296 | }, 297 | "outputId": "c2b83959-0b14-4d0b-918f-dab590cca53d" 298 | }, 299 | "source": [ 300 | "import time\n", 301 | "t1=time.time()\n", 302 | "!train_folder.py --root_dir \"alignn/examples/sample_data\" --epochs 3 --batch_size 2 --config \"alignn/examples/sample_data/config_example.json\" --output_dir=temp\n", 303 | "t2=time.time()\n", 304 | "print ('Time in s',t2-t1)" 305 | ], 306 | "execution_count": null, 307 | "outputs": [ 308 | { 309 | "output_type": "stream", 310 | "name": "stdout", 311 | "text": [ 312 | "DGL backend not selected or invalid. Assuming PyTorch for now.\n", 313 | "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable. Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n", 314 | "MAX val: 6.149\n", 315 | "MIN val: 0.0\n", 316 | "MAD: 1.0520696\n", 317 | "Baseline MAE: 0.7102749999999998\n", 318 | "data range 6.149 0.0\n", 319 | " 0% 0/40 [00:00\n", 1717 | "
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idtargetprediction
0POSCAR-JVASP-12918.vasp27.170000-6.187871
1POSCAR-JVASP-2035.vasp82.290001-7.342460
2POSCAR-JVASP-13942.vasp87.809998-7.392377
3POSCAR-JVASP-278.vasp144.320007-7.004450
4POSCAR-JVASP-10173.vasp33.700001-6.653560
............
76POSCAR-JVASP-4364.vasp54.290001-8.038940
77POSCAR-JVASP-29480.vasp78.639999-8.734681
78POSCAR-JVASP-28375.vasp55.480000-7.636129
79POSCAR-JVASP-590.vasp88.519997-7.158464
80POSCAR-JVASP-4741.vasp226.220001-5.179449
\n", 1810 | "

81 rows × 3 columns

\n", 1811 | "
\n", 1812 | " \n", 1822 | " \n", 1823 | " \n", 1860 | "\n", 1861 | " \n", 1885 | "
\n", 1886 | " \n", 1887 | " " 1888 | ] 1889 | }, 1890 | "metadata": {}, 1891 | "execution_count": 16 1892 | } 1893 | ] 1894 | }, 1895 | { 1896 | "cell_type": "code", 1897 | "source": [ 1898 | "!pip install matplotlib==3.1.3" 1899 | ], 1900 | "metadata": { 1901 | "id": "Sgg094jfO7Ol", 1902 | "outputId": "d0a4bff6-e8af-4fdb-8379-44371f0d542e", 1903 | "colab": { 1904 | "base_uri": "https://localhost:8080/", 1905 | "height": 469 1906 | } 1907 | }, 1908 | "execution_count": null, 1909 | "outputs": [ 1910 | { 1911 | "output_type": "stream", 1912 | "name": "stdout", 1913 | "text": [ 1914 | "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", 1915 | "Collecting matplotlib==3.1.3\n", 1916 | " Downloading matplotlib-3.1.3-cp37-cp37m-manylinux1_x86_64.whl (13.1 MB)\n", 1917 | "\u001b[K |████████████████████████████████| 13.1 MB 5.3 MB/s \n", 1918 | "\u001b[?25hRequirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (2.4.7)\n", 1919 | "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (1.21.6)\n", 1920 | "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (1.4.4)\n", 1921 | "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (2.8.2)\n", 1922 | "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.1.3) (0.11.0)\n", 1923 | "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib==3.1.3) (4.1.1)\n", 1924 | "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib==3.1.3) (1.15.0)\n", 1925 | "Installing collected packages: matplotlib\n", 1926 | " Attempting uninstall: matplotlib\n", 1927 | " Found existing installation: matplotlib 3.5.3\n", 1928 | " Uninstalling matplotlib-3.5.3:\n", 1929 | " Successfully uninstalled matplotlib-3.5.3\n", 1930 | "\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", 1931 | "alignn 2022.9.22 requires matplotlib>=3.4.1, but you have matplotlib 3.1.3 which is incompatible.\u001b[0m\n", 1932 | "Successfully installed matplotlib-3.1.3\n" 1933 | ] 1934 | }, 1935 | { 1936 | "output_type": "display_data", 1937 | "data": { 1938 | "application/vnd.colab-display-data+json": { 1939 | "pip_warning": { 1940 | "packages": [ 1941 | "matplotlib", 1942 | "mpl_toolkits" 1943 | ] 1944 | } 1945 | } 1946 | }, 1947 | "metadata": {} 1948 | } 1949 | ] 1950 | }, 1951 | { 1952 | "cell_type": "code", 1953 | "source": [ 1954 | "import matplotlib.pyplot as plt\n", 1955 | "%matplotlib inline\n", 1956 | "plt.plot(df['target'],df['prediction'],'.')\n", 1957 | "plt.plot(df['target'],df['target'],'-.')\n", 1958 | "plt.xlabel('DFT data (meV)')\n", 1959 | "plt.ylabel('ALIGNN predictions (meV)')" 1960 | ], 1961 | "metadata": { 1962 | "colab": { 1963 | "base_uri": "https://localhost:8080/", 1964 | "height": 297 1965 | }, 1966 | "id": "hf3vWrmQpDwg", 1967 | "outputId": "50090b7e-eece-46fc-c2a2-90379941d20e" 1968 | }, 1969 | "execution_count": null, 1970 | "outputs": [ 1971 | { 1972 | "output_type": "execute_result", 1973 | "data": { 1974 | "text/plain": [ 1975 | "Text(0, 0.5, 'ALIGNN predictions (meV)')" 1976 | ] 1977 | }, 1978 | "metadata": {}, 1979 | "execution_count": 21 1980 | }, 1981 | { 1982 | "output_type": "display_data", 1983 | "data": { 1984 | "text/plain": [ 1985 | "
" 1986 | ], 1987 | "image/png": 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\n" 1988 | }, 1989 | "metadata": { 1990 | "needs_background": "light" 1991 | } 1992 | } 1993 | ] 1994 | }, 1995 | { 1996 | "cell_type": "code", 1997 | "source": [ 1998 | "# from jarvis.db.figshare import data as jdata\n", 1999 | "# from jarvis.core.atoms import Atoms\n", 2000 | "# import os\n", 2001 | "\n", 2002 | "# temp_dir_name = \"DataDir_QM9_U0\" \n", 2003 | "# os.makedirs(temp_dir_name)\n", 2004 | "# os.chdir(temp_dir_name)\n", 2005 | "\n", 2006 | "# dft_3d = jdata(\"qm9_std_jctc\") #jdata(\"dft_3d\")\n", 2007 | "# prop = \"U0\" #\"exfoliation_energy\" #\"optb88vdw_bandgap\"\n", 2008 | "# f = open(\"id_prop.csv\", \"w\")\n", 2009 | "# # count = 0\n", 2010 | "# for i in dft_3d:\n", 2011 | "# atoms = Atoms.from_dict(i[\"atoms\"])\n", 2012 | "# jid = i[\"id\"]\n", 2013 | "# poscar_name = \"POSCAR-\" + jid + \".vasp\"\n", 2014 | "# target = i[prop]\n", 2015 | "# if target != \"na\":\n", 2016 | "# atoms.write_poscar(poscar_name)\n", 2017 | "# f.write(\"%s,%6f\\n\" % (poscar_name, target))\n", 2018 | "# # count += 1\n", 2019 | "# # if count == max_samples:\n", 2020 | "# # break\n", 2021 | "# f.close()\n", 2022 | "\n", 2023 | "# os.chdir(cwd)" 2024 | ], 2025 | "metadata": { 2026 | "id": "cOSVNX6zUeXd" 2027 | }, 2028 | "execution_count": null, 2029 | "outputs": [] 2030 | }, 2031 | { 2032 | "cell_type": "code", 2033 | "source": [ 2034 | "pwd" 2035 | ], 2036 | "metadata": { 2037 | "colab": { 2038 | "base_uri": "https://localhost:8080/", 2039 | "height": 35 2040 | }, 2041 | "id": "5SxYZoRkqM80", 2042 | "outputId": "519dbaa7-3922-4d4b-c962-3a7ccf4fc475" 2043 | }, 2044 | "execution_count": null, 2045 | "outputs": [ 2046 | { 2047 | "output_type": "execute_result", 2048 | "data": { 2049 | "text/plain": [ 2050 | "'/content/alignn/DataDir_QM9_U0'" 2051 | ], 2052 | "application/vnd.google.colaboratory.intrinsic+json": { 2053 | "type": "string" 2054 | } 2055 | }, 2056 | "metadata": {}, 2057 | "execution_count": 23 2058 | } 2059 | ] 2060 | }, 2061 | { 2062 | "cell_type": "code", 2063 | "source": [ 2064 | "!ls" 2065 | ], 2066 | "metadata": { 2067 | "id": "wDQTE2p1ZMAh" 2068 | }, 2069 | "execution_count": null, 2070 | "outputs": [] 2071 | }, 2072 | { 2073 | "cell_type": "markdown", 2074 | "source": [ 2075 | "Changing the cut-off in config_example.json as 5, n_train as 110000, n_val as 10000, n_test as 10829" 2076 | ], 2077 | "metadata": { 2078 | "id": "y-NnYsVoZght" 2079 | } 2080 | }, 2081 | { 2082 | "cell_type": "code", 2083 | "source": [ 2084 | "# from jarvis.db.jsonutils import loadjson, dumpjson\n", 2085 | "# config = loadjson(\"alignn/examples/sample_data/config_example.json\")\n", 2086 | "# config['cutoff'] = 5.0\n", 2087 | "# config['n_train'] = 110000\n", 2088 | "# config['n_val'] = 10000\n", 2089 | "# config['n_test'] = 10829\n", 2090 | "# dumpjson(data=config, filename=\"config_qm9.json\")" 2091 | ], 2092 | "metadata": { 2093 | "id": "gqBCdqVCaBmy" 2094 | }, 2095 | "execution_count": null, 2096 | "outputs": [] 2097 | }, 2098 | { 2099 | "cell_type": "markdown", 2100 | "source": [ 2101 | "Run for 1000 epochs instead of 5 here to get reasonable performance/MAE" 2102 | ], 2103 | "metadata": { 2104 | "id": "WasOday5a72c" 2105 | } 2106 | }, 2107 | { 2108 | "cell_type": "code", 2109 | "source": [ 2110 | "# import time\n", 2111 | "# t1=time.time()\n", 2112 | "# !train_folder.py --root_dir \"DataDir_QM9_U0\" --epochs 5 --batch_size 64 --config \"config_qm9.json\" --output_dir=\"DataDir_QM9U0out\"\n", 2113 | "# t2=time.time()\n", 2114 | "# print ('Time in s',t2-t1)" 2115 | ], 2116 | "metadata": { 2117 | "id": "acaMqZP_XbBr" 2118 | }, 2119 | "execution_count": null, 2120 | "outputs": [] 2121 | }, 2122 | { 2123 | "cell_type": "code", 2124 | "source": [ 2125 | "!pip freeze" 2126 | ], 2127 | "metadata": { 2128 | "colab": { 2129 | "base_uri": "https://localhost:8080/" 2130 | }, 2131 | "id": "rqaGpi_PYpjw", 2132 | "outputId": "47d113b9-f400-48d0-8f05-dd53c84b4a9f" 2133 | }, 2134 | "execution_count": null, 2135 | "outputs": [ 2136 | { 2137 | "output_type": "stream", 2138 | "name": "stdout", 2139 | "text": [ 2140 | "absl-py==1.3.0\n", 2141 | "aeppl==0.0.33\n", 2142 | "aesara==2.7.9\n", 2143 | "aiohttp==3.8.3\n", 2144 | "aiosignal==1.2.0\n", 2145 | "alabaster==0.7.12\n", 2146 | "albumentations==1.2.1\n", 2147 | "alignn==2022.9.22\n", 2148 | "altair==4.2.0\n", 2149 | "appdirs==1.4.4\n", 2150 | "arviz==0.12.1\n", 2151 | "astor==0.8.1\n", 2152 | "astropy==4.3.1\n", 2153 | "astunparse==1.6.3\n", 2154 | "async-timeout==4.0.2\n", 2155 | "asynctest==0.13.0\n", 2156 | "atari-py==0.2.9\n", 2157 | "atomicwrites==1.4.1\n", 2158 | "attrs==22.1.0\n", 2159 | "audioread==3.0.0\n", 2160 | "autograd==1.5\n", 2161 | "Babel==2.10.3\n", 2162 | "backcall==0.2.0\n", 2163 | "beautifulsoup4==4.6.3\n", 2164 | "bleach==5.0.1\n", 2165 | "blis==0.7.8\n", 2166 | "bokeh==2.3.3\n", 2167 | "branca==0.5.0\n", 2168 | "bs4==0.0.1\n", 2169 | "CacheControl==0.12.11\n", 2170 | "cached-property==1.5.2\n", 2171 | "cachetools==4.2.4\n", 2172 | "catalogue==2.0.8\n", 2173 | "certifi==2022.9.24\n", 2174 | "cffi==1.15.1\n", 2175 | "cftime==1.6.2\n", 2176 | "chardet==3.0.4\n", 2177 | "charset-normalizer==2.1.1\n", 2178 | "click==7.1.2\n", 2179 | "clikit==0.6.2\n", 2180 | "cloudpickle==1.5.0\n", 2181 | "cmake==3.22.6\n", 2182 | "cmdstanpy==1.0.7\n", 2183 | "colorcet==3.0.1\n", 2184 | "colorlover==0.3.0\n", 2185 | "community==1.0.0b1\n", 2186 | "confection==0.0.3\n", 2187 | "cons==0.4.5\n", 2188 | "contextlib2==0.5.5\n", 2189 | "convertdate==2.4.0\n", 2190 | "crashtest==0.3.1\n", 2191 | "crcmod==1.7\n", 2192 | "cufflinks==0.17.3\n", 2193 | "cvxopt==1.3.0\n", 2194 | "cvxpy==1.2.1\n", 2195 | "cycler==0.11.0\n", 2196 | "cymem==2.0.7\n", 2197 | "Cython==0.29.32\n", 2198 | "daft==0.0.4\n", 2199 | "dask==2022.2.0\n", 2200 | "datascience==0.17.5\n", 2201 | "debugpy==1.0.0\n", 2202 | "decorator==4.4.2\n", 2203 | "defusedxml==0.7.1\n", 2204 | "descartes==1.1.0\n", 2205 | "dgl==0.9.1\n", 2206 | "dill==0.3.5.1\n", 2207 | "distributed==2022.2.0\n", 2208 | "dlib==19.24.0\n", 2209 | "dm-tree==0.1.7\n", 2210 | "docutils==0.17.1\n", 2211 | "dopamine-rl==1.0.5\n", 2212 | "earthengine-api==0.1.327\n", 2213 | "easydict==1.10\n", 2214 | "ecos==2.0.10\n", 2215 | "editdistance==0.5.3\n", 2216 | "en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.0/en_core_web_sm-3.4.0-py3-none-any.whl\n", 2217 | "entrypoints==0.4\n", 2218 | "ephem==4.1.3\n", 2219 | "et-xmlfile==1.1.0\n", 2220 | "etils==0.8.0\n", 2221 | "etuples==0.3.8\n", 2222 | "fa2==0.3.5\n", 2223 | "fastai==2.7.9\n", 2224 | "fastcore==1.5.27\n", 2225 | "fastdownload==0.0.7\n", 2226 | "fastdtw==0.3.4\n", 2227 | "fastjsonschema==2.16.2\n", 2228 | "fastprogress==1.0.3\n", 2229 | "fastrlock==0.8\n", 2230 | "feather-format==0.4.1\n", 2231 | "filelock==3.8.0\n", 2232 | "firebase-admin==4.4.0\n", 2233 | "fix-yahoo-finance==0.0.22\n", 2234 | "flake8==5.0.4\n", 2235 | "Flask==1.1.4\n", 2236 | "flatbuffers==1.12\n", 2237 | "folium==0.12.1.post1\n", 2238 | "fonttools==4.37.4\n", 2239 | "frozenlist==1.3.1\n", 2240 | "fsspec==2022.8.2\n", 2241 | "future==0.16.0\n", 2242 | "gast==0.4.0\n", 2243 | "GDAL==2.2.2\n", 2244 | "gdown==4.4.0\n", 2245 | "gensim==3.6.0\n", 2246 | "geographiclib==1.52\n", 2247 | "geopy==1.17.0\n", 2248 | "gin-config==0.5.0\n", 2249 | "glob2==0.7\n", 2250 | "google==2.0.3\n", 2251 | "google-api-core==1.31.6\n", 2252 | "google-api-python-client==1.12.11\n", 2253 | "google-auth==1.35.0\n", 2254 | "google-auth-httplib2==0.0.4\n", 2255 | "google-auth-oauthlib==0.4.6\n", 2256 | "google-cloud-bigquery==1.21.0\n", 2257 | "google-cloud-bigquery-storage==1.1.2\n", 2258 | "google-cloud-core==1.0.3\n", 2259 | "google-cloud-datastore==1.8.0\n", 2260 | "google-cloud-firestore==1.7.0\n", 2261 | "google-cloud-language==1.2.0\n", 2262 | "google-cloud-storage==1.18.1\n", 2263 | "google-cloud-translate==1.5.0\n", 2264 | "google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz\n", 2265 | "google-pasta==0.2.0\n", 2266 | "google-resumable-media==0.4.1\n", 2267 | "googleapis-common-protos==1.56.4\n", 2268 | "googledrivedownloader==0.4\n", 2269 | "graphviz==0.10.1\n", 2270 | "greenlet==1.1.3.post0\n", 2271 | "grpcio==1.49.1\n", 2272 | "gspread==3.4.2\n", 2273 | "gspread-dataframe==3.0.8\n", 2274 | "gym==0.25.2\n", 2275 | "gym-notices==0.0.8\n", 2276 | "h5py==3.1.0\n", 2277 | "HeapDict==1.0.1\n", 2278 | "hijri-converter==2.2.4\n", 2279 | "holidays==0.16\n", 2280 | "holoviews==1.14.9\n", 2281 | "html5lib==1.0.1\n", 2282 | "httpimport==0.5.18\n", 2283 | "httplib2==0.17.4\n", 2284 | "httplib2shim==0.0.3\n", 2285 | "httpstan==4.6.1\n", 2286 | "humanize==0.5.1\n", 2287 | "hyperopt==0.1.2\n", 2288 | "idna==2.10\n", 2289 | "imageio==2.9.0\n", 2290 | "imagesize==1.4.1\n", 2291 | "imbalanced-learn==0.8.1\n", 2292 | "imblearn==0.0\n", 2293 | "imgaug==0.4.0\n", 2294 | "importlib-metadata==4.2.0\n", 2295 | "importlib-resources==5.10.0\n", 2296 | "imutils==0.5.4\n", 2297 | "inflect==2.1.0\n", 2298 | "intel-openmp==2022.2.0\n", 2299 | "intervaltree==2.1.0\n", 2300 | "ipykernel==5.3.4\n", 2301 | "ipython==7.9.0\n", 2302 | "ipython-genutils==0.2.0\n", 2303 | "ipython-sql==0.3.9\n", 2304 | "ipywidgets==7.7.1\n", 2305 | "itsdangerous==1.1.0\n", 2306 | "jarvis-tools==2022.9.16\n", 2307 | "jax==0.3.23\n", 2308 | "jaxlib @ https://storage.googleapis.com/jax-releases/cuda11/jaxlib-0.3.22+cuda11.cudnn805-cp37-cp37m-manylinux2014_x86_64.whl\n", 2309 | "jieba==0.42.1\n", 2310 | "Jinja2==2.11.3\n", 2311 | "joblib==1.2.0\n", 2312 | "jpeg4py==0.1.4\n", 2313 | "jsonschema==4.3.3\n", 2314 | "jupyter-client==6.1.12\n", 2315 | "jupyter-console==6.1.0\n", 2316 | "jupyter-core==4.11.1\n", 2317 | "jupyterlab-widgets==3.0.3\n", 2318 | "kaggle==1.5.12\n", 2319 | "kapre==0.3.7\n", 2320 | "keras==2.9.0\n", 2321 | "Keras-Preprocessing==1.1.2\n", 2322 | "keras-vis==0.4.1\n", 2323 | "kiwisolver==1.4.4\n", 2324 | "korean-lunar-calendar==0.3.1\n", 2325 | "langcodes==3.3.0\n", 2326 | "libclang==14.0.6\n", 2327 | "librosa==0.8.1\n", 2328 | "lightgbm==2.2.3\n", 2329 | "llvmlite==0.39.1\n", 2330 | "lmdb==0.99\n", 2331 | "locket==1.0.0\n", 2332 | "logical-unification==0.4.5\n", 2333 | "LunarCalendar==0.0.9\n", 2334 | "lxml==4.9.1\n", 2335 | "Markdown==3.4.1\n", 2336 | "MarkupSafe==2.0.1\n", 2337 | "marshmallow==3.18.0\n", 2338 | "matplotlib==3.1.3\n", 2339 | "matplotlib-venn==0.11.7\n", 2340 | "mccabe==0.7.0\n", 2341 | "miniKanren==1.0.3\n", 2342 | "missingno==0.5.1\n", 2343 | "mistune==0.8.4\n", 2344 | "mizani==0.7.3\n", 2345 | "mkl==2019.0\n", 2346 | "mlxtend==0.14.0\n", 2347 | "more-itertools==8.14.0\n", 2348 | "moviepy==0.2.3.5\n", 2349 | "mpmath==1.2.1\n", 2350 | "msgpack==1.0.4\n", 2351 | "multidict==6.0.2\n", 2352 | "multipledispatch==0.6.0\n", 2353 | "multitasking==0.0.11\n", 2354 | "murmurhash==1.0.9\n", 2355 | "music21==5.5.0\n", 2356 | "natsort==5.5.0\n", 2357 | "nbconvert==5.6.1\n", 2358 | "nbformat==5.7.0\n", 2359 | "netCDF4==1.6.1\n", 2360 | "networkx==2.6.3\n", 2361 | "nibabel==3.0.2\n", 2362 | "nltk==3.7\n", 2363 | "notebook==5.5.0\n", 2364 | "numba==0.56.3\n", 2365 | "numexpr==2.8.3\n", 2366 | "numpy==1.21.6\n", 2367 | "oauth2client==4.1.3\n", 2368 | "oauthlib==3.2.1\n", 2369 | "okgrade==0.4.3\n", 2370 | "opencv-contrib-python==4.6.0.66\n", 2371 | "opencv-python==4.6.0.66\n", 2372 | "opencv-python-headless==4.6.0.66\n", 2373 | "openpyxl==3.0.10\n", 2374 | "opt-einsum==3.3.0\n", 2375 | "osqp==0.6.2.post0\n", 2376 | "packaging==21.3\n", 2377 | "palettable==3.3.0\n", 2378 | "pandas==1.3.5\n", 2379 | "pandas-datareader==0.9.0\n", 2380 | "pandas-gbq==0.13.3\n", 2381 | "pandas-profiling==1.4.1\n", 2382 | "pandocfilters==1.5.0\n", 2383 | "panel==0.12.1\n", 2384 | "param==1.12.2\n", 2385 | "parso==0.8.3\n", 2386 | "partd==1.3.0\n", 2387 | "pastel==0.2.1\n", 2388 | "pathlib==1.0.1\n", 2389 | "pathy==0.6.2\n", 2390 | "patsy==0.5.3\n", 2391 | "pep517==0.13.0\n", 2392 | "pexpect==4.8.0\n", 2393 | "pickleshare==0.7.5\n", 2394 | "Pillow==7.1.2\n", 2395 | "pip-tools==6.2.0\n", 2396 | "plotly==5.5.0\n", 2397 | "plotnine==0.8.0\n", 2398 | "pluggy==0.7.1\n", 2399 | "pooch==1.6.0\n", 2400 | "portpicker==1.3.9\n", 2401 | "prefetch-generator==1.0.1\n", 2402 | "preshed==3.0.8\n", 2403 | "prettytable==3.4.1\n", 2404 | "progressbar2==3.38.0\n", 2405 | "promise==2.3\n", 2406 | "prompt-toolkit==2.0.10\n", 2407 | "prophet==1.1.1\n", 2408 | "protobuf==3.17.3\n", 2409 | "psutil==5.9.3\n", 2410 | "psycopg2==2.9.4\n", 2411 | "ptyprocess==0.7.0\n", 2412 | "py==1.11.0\n", 2413 | "pyarrow==6.0.1\n", 2414 | "pyasn1==0.4.8\n", 2415 | "pyasn1-modules==0.2.8\n", 2416 | "pycocotools==2.0.5\n", 2417 | "pycodestyle==2.9.1\n", 2418 | "pycparser==2.21\n", 2419 | "pyct==0.4.8\n", 2420 | "pydantic==1.9.2\n", 2421 | "pydata-google-auth==1.4.0\n", 2422 | "pydocstyle==6.1.1\n", 2423 | "pydot==1.3.0\n", 2424 | "pydot-ng==2.0.0\n", 2425 | "pydotplus==2.0.2\n", 2426 | "PyDrive==1.3.1\n", 2427 | "pyemd==0.5.1\n", 2428 | "pyerfa==2.0.0.1\n", 2429 | "pyflakes==2.5.0\n", 2430 | "Pygments==2.6.1\n", 2431 | "pygobject==3.26.1\n", 2432 | "pylev==1.4.0\n", 2433 | "pymc==4.1.4\n", 2434 | "PyMeeus==0.5.11\n", 2435 | "pymongo==4.2.0\n", 2436 | "pymystem3==0.2.0\n", 2437 | "PyOpenGL==3.1.6\n", 2438 | "pyparsing==2.4.7\n", 2439 | "pyrsistent==0.18.1\n", 2440 | "pysimdjson==3.2.0\n", 2441 | "pysndfile==1.3.8\n", 2442 | "PySocks==1.7.1\n", 2443 | "pystan==3.3.0\n", 2444 | "pytest==3.6.4\n", 2445 | "python-apt==0.0.0\n", 2446 | "python-chess==0.23.11\n", 2447 | "python-dateutil==2.8.2\n", 2448 | "python-louvain==0.16\n", 2449 | "python-slugify==6.1.2\n", 2450 | "python-utils==3.3.3\n", 2451 | "pytorch-ignite==0.4.10\n", 2452 | "pytz==2022.4\n", 2453 | "pyviz-comms==2.2.1\n", 2454 | "PyWavelets==1.3.0\n", 2455 | "PyYAML==6.0\n", 2456 | "pyzmq==23.2.1\n", 2457 | "qdldl==0.1.5.post2\n", 2458 | "qudida==0.0.4\n", 2459 | "regex==2022.6.2\n", 2460 | "requests==2.23.0\n", 2461 | "requests-oauthlib==1.3.1\n", 2462 | "resampy==0.4.2\n", 2463 | "rpy2==3.4.5\n", 2464 | "rsa==4.9\n", 2465 | "scikit-image==0.18.3\n", 2466 | "scikit-learn==1.0.2\n", 2467 | "scipy==1.7.3\n", 2468 | "screen-resolution-extra==0.0.0\n", 2469 | "scs==3.2.0\n", 2470 | "seaborn==0.11.2\n", 2471 | "Send2Trash==1.8.0\n", 2472 | "setuptools-git==1.2\n", 2473 | "Shapely==1.8.5.post1\n", 2474 | "six==1.15.0\n", 2475 | "sklearn-pandas==1.8.0\n", 2476 | "smart-open==5.2.1\n", 2477 | "snowballstemmer==2.2.0\n", 2478 | "sortedcontainers==2.4.0\n", 2479 | "soundfile==0.11.0\n", 2480 | "spacy==3.4.1\n", 2481 | "spacy-legacy==3.0.10\n", 2482 | "spacy-loggers==1.0.3\n", 2483 | "spglib==2.0.1\n", 2484 | "Sphinx==1.8.6\n", 2485 | "sphinxcontrib-serializinghtml==1.1.5\n", 2486 | "sphinxcontrib-websupport==1.2.4\n", 2487 | "SQLAlchemy==1.4.41\n", 2488 | "sqlparse==0.4.3\n", 2489 | "srsly==2.4.4\n", 2490 | "statsmodels==0.12.2\n", 2491 | "sympy==1.7.1\n", 2492 | "tables==3.7.0\n", 2493 | "tabulate==0.8.10\n", 2494 | "tblib==1.7.0\n", 2495 | "tenacity==8.1.0\n", 2496 | "tensorboard==2.9.1\n", 2497 | "tensorboard-data-server==0.6.1\n", 2498 | "tensorboard-plugin-wit==1.8.1\n", 2499 | "tensorflow==2.9.2\n", 2500 | "tensorflow-datasets==4.6.0\n", 2501 | "tensorflow-estimator==2.9.0\n", 2502 | "tensorflow-gcs-config==2.9.1\n", 2503 | "tensorflow-hub==0.12.0\n", 2504 | "tensorflow-io-gcs-filesystem==0.27.0\n", 2505 | "tensorflow-metadata==1.10.0\n", 2506 | "tensorflow-probability==0.16.0\n", 2507 | "termcolor==2.0.1\n", 2508 | "terminado==0.13.3\n", 2509 | "testpath==0.6.0\n", 2510 | "text-unidecode==1.3\n", 2511 | "textblob==0.15.3\n", 2512 | "thinc==8.1.4\n", 2513 | "threadpoolctl==3.1.0\n", 2514 | "tifffile==2021.11.2\n", 2515 | "toml==0.10.2\n", 2516 | "tomli==2.0.1\n", 2517 | "toolz==0.12.0\n", 2518 | "torch @ https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp37-cp37m-linux_x86_64.whl\n", 2519 | "torchaudio @ https://download.pytorch.org/whl/cu113/torchaudio-0.12.1%2Bcu113-cp37-cp37m-linux_x86_64.whl\n", 2520 | "torchsummary==1.5.1\n", 2521 | "torchtext==0.13.1\n", 2522 | "torchvision @ https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp37-cp37m-linux_x86_64.whl\n", 2523 | "tornado==5.1.1\n", 2524 | "tqdm==4.64.1\n", 2525 | "traitlets==5.1.1\n", 2526 | "tweepy==3.10.0\n", 2527 | "typeguard==2.7.1\n", 2528 | "typer==0.4.2\n", 2529 | "typing-extensions==4.1.1\n", 2530 | "tzlocal==1.5.1\n", 2531 | "ujson==5.5.0\n", 2532 | "uritemplate==3.0.1\n", 2533 | "urllib3==1.24.3\n", 2534 | "vega-datasets==0.9.0\n", 2535 | "wasabi==0.10.1\n", 2536 | "wcwidth==0.2.5\n", 2537 | "webargs==8.2.0\n", 2538 | "webencodings==0.5.1\n", 2539 | "Werkzeug==1.0.1\n", 2540 | "widgetsnbextension==3.6.1\n", 2541 | "wordcloud==1.8.2.2\n", 2542 | "wrapt==1.14.1\n", 2543 | "xarray==0.20.2\n", 2544 | "xarray-einstats==0.2.2\n", 2545 | "xgboost==0.90\n", 2546 | "xkit==0.0.0\n", 2547 | "xlrd==1.1.0\n", 2548 | "xlwt==1.3.0\n", 2549 | "xmltodict==0.13.0\n", 2550 | "yarl==1.8.1\n", 2551 | "yellowbrick==1.5\n", 2552 | "zict==2.2.0\n", 2553 | "zipp==3.9.0\n" 2554 | ] 2555 | } 2556 | ] 2557 | }, 2558 | { 2559 | "cell_type": "code", 2560 | "source": [], 2561 | "metadata": { 2562 | "id": "arZZybt0Nw_Z" 2563 | }, 2564 | "execution_count": null, 2565 | "outputs": [] 2566 | } 2567 | ] 2568 | } -------------------------------------------------------------------------------- /alignn/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /intro_lecture/intro_to_gnns_apsmarch23.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/williamratcliff/GNN-tutorial-APS-March-2023/d7a8b65083bb80595cf14d9404080b3f3e50b286/intro_lecture/intro_to_gnns_apsmarch23.pdf --------------------------------------------------------------------------------