{"@type": "dcat:Dataset", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Peter St. John", "hasEmail": "mailto:peter.stjohn@nlr.gov"}, "dataQuality": true, "description": "A database of quantum mechanical calculations on organic photovoltaic candidate molecules. Related Publications: Peter C. St. John, Caleb Phillips, Travis W. Kemper, A. Nolan Wilson, Michael F. Crowley, Mark R. Nimlos, Ross E. Larsen. (2018) Message-passing neural networks for high-throughput polymer screening arXiv:1807.10363", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://data.nlr.gov/system/files/236/1712697052-mol_test.csv.gz", "description": "Test set data for SchNet-like models train on optimized 3D geometry.", "mediaType": "application/octet-stream", "title": "Test set data for SchNet-like models train on optimized 3D geometry."}, {"@type": "dcat:Distribution", "accessURL": "https://data.nlr.gov/system/files/236/1712697052-mol_test_uff.csv.gz", "description": "Test set data for SchNet-like models, but with the 3D geometry generated via DFT replaced by an approximate 3D structure optimized from the SMILES string using UFF as implemented in rdkit.", "mediaType": "application/octet-stream", "title": "Test set data for SchNet-like models, but with the 3D geometry generated via DFT replaced by an approximate 3D structure optimized from the SMILES string using UFF as implemented in rdkit."}, {"@type": "dcat:Distribution", "accessURL": "https://data.nlr.gov/system/files/236/1712697052-mol_train.csv.gz", "description": "Training set data for SchNet-like models train on optimized 3D geometry.", "mediaType": "application/octet-stream", "title": "Training set data for SchNet-like models train on optimized 3D geometry."}, {"@type": "dcat:Distribution", "accessURL": "https://data.nlr.gov/system/files/236/1712697052-mol_valid.csv.gz", "description": "Validation set data for SchNet-like models train on optimized 3D geometry.", "mediaType": "application/octet-stream", "title": "Validation set data for SchNet-like models train on optimized 3D geometry."}, {"@type": "dcat:Distribution", "accessURL": "https://data.nlr.gov/system/files/236/1712697052-opv_db.csv.gz", "description": "DFT calculations performed with a variety of functiona/basis set combinations.\r\n\r\nFormat:\r\nThe data is stored as a gzipped csv file. The fields are as follows:\r\n\r\nmol: a SDF-style string containing the optimized 3D molecule coordinates\r\nctag: A tag for molecule identification (unused)\r\nbasis: the DFT functional / basis set combination\r\ntotal_energy\r\noptical_lumo\r\ngap\r\nhomo\r\nlumo\r\nspectral_overlap\r\ndelta_homo\r\ndelta_lumo\r\ndelta_optical_lumo\r\nhomo_extrapolated\r\nlumo_extrapolated\r\ngap_extrapolated\r\noptical_lumo_extrapolated\r\nsmile: a canonicalized SMILES string representation of the molecule's 2D geometry", "mediaType": "application/octet-stream", "title": "DFT calculations performed with a variety of functiona/basis set combinations.\r\n\r\nFormat:\r\nThe data is stored as a gzipped csv file. The fields are as follows:\r\n\r\nmol: a SDF-style string containing the optimized 3D molecule coordinates\r\nctag: A tag for molecule identification (unused)\r\nbasis: the DFT functional / basis set combination\r\ntotal_energy\r\noptical_lumo\r\ngap\r\nhomo\r\nlumo\r\nspectral_overlap\r\ndelta_homo\r\ndelta_lumo\r\ndelta_optical_lumo\r\nhomo_extrapolated\r\nlumo_extrapolated\r\ngap_extrapolated\r\noptical_lumo_extrapolated\r\nsmile: a canonicalized SMILES string representation of the molecule's 2D geometry"}, {"@type": "dcat:Distribution", "accessURL": "https://data.nlr.gov/system/files/236/1712697052-smiles_test.csv.gz", "description": "Test set data for the models considering only unique SMILES strings as molecule inputs.", "mediaType": "application/octet-stream", "title": "Test set data for the models considering only unique SMILES strings as molecule inputs."}, {"@type": "dcat:Distribution", "accessURL": "https://data.nlr.gov/system/files/236/1712697052-smiles_train.csv.gz", "description": "Training set data for the models considering only unique SMILES strings as molecule inputs.", "mediaType": "application/octet-stream", "title": "Training set data for the models considering only unique SMILES strings as molecule inputs."}, {"@type": "dcat:Distribution", "accessURL": "https://data.nlr.gov/system/files/236/1712697052-smiles_valid.csv.gz", "description": "Validation set data for the models considering only unique SMILES strings as molecule inputs.", "mediaType": "application/octet-stream", "title": "Validation set data for the models considering only unique SMILES strings as molecule inputs."}], "identifier": "https://data.openei.org/submissions/8285", "issued": "2024-04-10T17:18:04Z", "keyword": ["OPV", "organic photovoltaic"], "landingPage": "https://data.nlr.gov/submissions/236", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2026-03-12T18:10:31Z", "programCode": ["019:023", "019:009", "019:008"], "projectNumber": "", "projectTitle": "", "publisher": {"@type": "org:Organization", "name": "National Renewable Energy Laboratory"}, "title": "Organic Photovoltaic (OPV) Database"}