{"id":16511323,"url":"https://github.com/pbenner/pycoordinationnet","last_synced_at":"2025-11-26T19:04:05.885Z","repository":{"id":189202293,"uuid":"680146371","full_name":"pbenner/pycoordinationnet","owner":"pbenner","description":"A neural network model for materials property prediction based on coordination 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\"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from coordinationnet import CoordinationNet\\n\",\n    \"from coordinationnet import CoordinationFeaturesData\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"## Load MP Oxides data set\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data = CoordinationFeaturesData.load('data/mpoxides.dill')\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"## Create a new model instance\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model config:\\n\",\n      \"-\u003e composition          : False\\n\",\n      \"-\u003e sites                : False\\n\",\n      \"-\u003e sites_oxid           : False\\n\",\n      \"-\u003e sites_ces            : False\\n\",\n      \"-\u003e site_features        : True\\n\",\n      \"-\u003e site_features_ces    : True\\n\",\n      \"-\u003e site_features_oxid   : True\\n\",\n      \"-\u003e site_features_csms   : True\\n\",\n      \"-\u003e site_features_ligands: False\\n\",\n      \"-\u003e ligands              : False\\n\",\n      \"-\u003e ce_neighbors         : False\\n\",\n      \"\\n\",\n      \"Creating a transformer model with 12,277,945 parameters\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = CoordinationNet(max_epochs = 10)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"## Training, predicting, and cross-validation\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 9: 100%|██████████| 31/31 [00:01\u003c00:00, 22.34it/s, val_loss=0.192, train_loss=0.225]\\n\",\n      \"Model config:\\n\",\n      \"-\u003e composition          : False\\n\",\n      \"-\u003e sites                : False\\n\",\n      \"-\u003e sites_oxid           : False\\n\",\n      \"-\u003e sites_ces            : False\\n\",\n      \"-\u003e site_features        : True\\n\",\n      \"-\u003e site_features_ces    : True\\n\",\n      \"-\u003e site_features_oxid   : True\\n\",\n      \"-\u003e site_features_csms   : True\\n\",\n      \"-\u003e site_features_ligands: False\\n\",\n      \"-\u003e ligands              : False\\n\",\n      \"-\u003e ce_neighbors         : False\\n\",\n      \"\\n\",\n      \"Creating a transformer model with 12,277,945 parameters\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'best_val_error': 0.19231659173965454,\\n\",\n       \" 'train_error': [0.8821187615394592,\\n\",\n       \"  0.6863077282905579,\\n\",\n       \"  0.6475048065185547,\\n\",\n       \"  0.5556382536888123,\\n\",\n       \"  0.48192858695983887,\\n\",\n       \"  0.3381344974040985,\\n\",\n       \"  0.32037481665611267,\\n\",\n       \"  0.289189875125885,\\n\",\n       \"  0.2524196207523346,\\n\",\n       \"  0.2248552143573761],\\n\",\n       \" 'val_error': [0.6633234024047852,\\n\",\n       \"  0.6896966695785522,\\n\",\n       \"  0.6661754846572876,\\n\",\n       \"  0.5392132997512817,\\n\",\n       \"  0.46571817994117737,\\n\",\n       \"  0.32083022594451904,\\n\",\n       \"  0.27424806356430054,\\n\",\n       \"  0.2790601849555969,\\n\",\n       \"  0.2448316216468811,\\n\",\n       \"  0.20683790743350983,\\n\",\n       \"  0.19600436091423035]}\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.train(data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicting DataLoader 0: 100%|██████████| 35/35 [00:00\u003c00:00, 74.62it/s]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([[-2.3259],\\n\",\n       \"        [-3.2458],\\n\",\n       \"        [-3.7321],\\n\",\n       \"        ...,\\n\",\n       \"        [-2.1301],\\n\",\n       \"        [-2.1895],\\n\",\n       \"        [-1.7834]])\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.predict(data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training fold 1/3...\\n\",\n      \"Epoch 9: 100%|██████████| 21/21 [00:00\u003c00:00, 21.63it/s, val_loss=0.182, train_loss=0.176]\\n\",\n      \"Model config:\\n\",\n      \"-\u003e composition          : False\\n\",\n      \"-\u003e sites                : False\\n\",\n      \"-\u003e sites_oxid           : False\\n\",\n      \"-\u003e sites_ces            : False\\n\",\n      \"-\u003e site_features        : True\\n\",\n      \"-\u003e site_features_ces    : True\\n\",\n      \"-\u003e site_features_oxid   : True\\n\",\n      \"-\u003e site_features_csms   : True\\n\",\n      \"-\u003e site_features_ligands: False\\n\",\n      \"-\u003e ligands              : False\\n\",\n      \"-\u003e ce_neighbors         : False\\n\",\n      \"\\n\",\n      \"Creating a transformer model with 12,277,945 parameters\\n\",\n      \"Testing DataLoader 0: 100%|██████████| 12/12 [00:00\u003c00:00, 73.37it/s]\\n\",\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n      \"       Test metric             DataLoader 0\\n\",\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n      \"        test_loss           0.16995078325271606\\n\",\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n      \"Best validation score: 0.1810467690229416\\n\",\n      \"Training fold 2/3...\\n\",\n      \"Epoch 9: 100%|██████████| 21/21 [00:00\u003c00:00, 21.10it/s, val_loss=0.167, train_loss=0.163]\\n\",\n      \"Model config:\\n\",\n      \"-\u003e composition          : False\\n\",\n      \"-\u003e sites                : False\\n\",\n      \"-\u003e sites_oxid           : False\\n\",\n      \"-\u003e sites_ces            : False\\n\",\n      \"-\u003e site_features        : True\\n\",\n      \"-\u003e site_features_ces    : True\\n\",\n      \"-\u003e site_features_oxid   : True\\n\",\n      \"-\u003e site_features_csms   : True\\n\",\n      \"-\u003e site_features_ligands: False\\n\",\n      \"-\u003e ligands              : False\\n\",\n      \"-\u003e ce_neighbors         : False\\n\",\n      \"\\n\",\n      \"Creating a transformer model with 12,277,945 parameters\\n\",\n      \"Testing DataLoader 0: 100%|██████████| 12/12 [00:00\u003c00:00, 77.25it/s]\\n\",\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n      \"       Test metric             DataLoader 0\\n\",\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n      \"        test_loss           0.17509198188781738\\n\",\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n      \"Best validation score: 0.1643427014350891\\n\",\n      \"Training fold 3/3...\\n\",\n      \"Epoch 9: 100%|██████████| 21/21 [00:00\u003c00:00, 22.19it/s, val_loss=0.158, train_loss=0.173]\\n\",\n      \"Model config:\\n\",\n      \"-\u003e composition          : False\\n\",\n      \"-\u003e sites                : False\\n\",\n      \"-\u003e sites_oxid           : False\\n\",\n      \"-\u003e sites_ces            : False\\n\",\n      \"-\u003e site_features        : True\\n\",\n      \"-\u003e site_features_ces    : True\\n\",\n      \"-\u003e site_features_oxid   : True\\n\",\n      \"-\u003e site_features_csms   : True\\n\",\n      \"-\u003e site_features_ligands: False\\n\",\n      \"-\u003e ligands              : False\\n\",\n      \"-\u003e ce_neighbors         : False\\n\",\n      \"\\n\",\n      \"Creating a transformer model with 12,277,945 parameters\\n\",\n      \"Testing DataLoader 0: 100%|██████████| 12/12 [00:00\u003c00:00, 86.33it/s]\\n\",\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n      \"       Test metric             DataLoader 0\\n\",\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n      \"        test_loss           0.17175181210041046\\n\",\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n      \"Best validation score: 0.1518968790769577\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_loss, y, y_hat = model.cross_validation(data, 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.1722649782896042\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"crysfeat\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.8\"\n  },\n  \"orig_nbformat\": 4\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 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