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Organic Photovoltaic (OPV) Database

Published by National Renewable Energy Laboratory | Department of Energy | Metadata Last Checked: February 27, 2026 | Last Modified: 2026-02-26T18:22:21Z
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

Resources

8 resources available

  • Test set data for SchNet-like models train on optimized 3D geometry.

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  • 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.

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  • Training set data for SchNet-like models train on optimized 3D geometry.

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  • Validation set data for SchNet-like models train on optimized 3D geometry.

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  • DFT calculations performed with a variety of functiona/basis set combinations. Format: The data is stored as a gzipped csv file. The fields are as follows: mol: a SDF-style string containing the optimized 3D molecule coordinates ctag: A tag for molecule identification (unused) basis: the DFT functional / basis set combination total_energy optical_lumo gap homo lumo spectral_overlap delta_homo delta_lumo delta_optical_lumo homo_extrapolated lumo_extrapolated gap_extrapolated optical_lumo_extrapolated smile: a canonicalized SMILES string representation of the molecule's 2D geometry

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  • Test set data for the models considering only unique SMILES strings as molecule inputs.

    APPLICATION/OCTET-STREAM
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  • Training set data for the models considering only unique SMILES strings as molecule inputs.

    APPLICATION/OCTET-STREAM
    Visit Page

  • Validation set data for the models considering only unique SMILES strings as molecule inputs.

    APPLICATION/OCTET-STREAM
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Complete Metadata
@type dcat:Dataset
accessLevel public
bureauCode 
                                            [
  "019:20"
]
contactPoint 
                                            {
  "fn": "Peter St. John",
  "@type": "vcard:Contact",
  "hasEmail": "mailto:Peter.StJohn@nrel.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",
    "title": "Test set data for SchNet-like models train on optimized 3D geometry.",
    "accessURL": "https://data.nrel.gov/system/files/236/1712697052-mol_test.csv.gz",
    "mediaType": "application/octet-stream",
    "description": "Test set data for SchNet-like models train on optimized 3D geometry."
  },
  {
    "@type": "dcat:Distribution",
    "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.",
    "accessURL": "https://data.nrel.gov/system/files/236/1712697052-mol_test_uff.csv.gz",
    "mediaType": "application/octet-stream",
    "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."
  },
  {
    "@type": "dcat:Distribution",
    "title": "Training set data for SchNet-like models train on optimized 3D geometry.",
    "accessURL": "https://data.nrel.gov/system/files/236/1712697052-mol_train.csv.gz",
    "mediaType": "application/octet-stream",
    "description": "Training set data for SchNet-like models train on optimized 3D geometry."
  },
  {
    "@type": "dcat:Distribution",
    "title": "Validation set data for SchNet-like models train on optimized 3D geometry.",
    "accessURL": "https://data.nrel.gov/system/files/236/1712697052-mol_valid.csv.gz",
    "mediaType": "application/octet-stream",
    "description": "Validation set data for SchNet-like models train on optimized 3D geometry."
  },
  {
    "@type": "dcat:Distribution",
    "title": "DFT calculations performed with a variety of functiona/basis set combinations.

Format:
The data is stored as a gzipped csv file. The fields are as follows:

mol: a SDF-style string containing the optimized 3D molecule coordinates
ctag: A tag for molecule identification (unused)
basis: the DFT functional / basis set combination
total_energy
optical_lumo
gap
homo
lumo
spectral_overlap
delta_homo
delta_lumo
delta_optical_lumo
homo_extrapolated
lumo_extrapolated
gap_extrapolated
optical_lumo_extrapolated
smile: a canonicalized SMILES string representation of the molecule's 2D geometry",
    "accessURL": "https://data.nrel.gov/system/files/236/1712697052-opv_db.csv.gz",
    "mediaType": "application/octet-stream",
    "description": "DFT calculations performed with a variety of functiona/basis set combinations.

Format:
The data is stored as a gzipped csv file. The fields are as follows:

mol: a SDF-style string containing the optimized 3D molecule coordinates
ctag: A tag for molecule identification (unused)
basis: the DFT functional / basis set combination
total_energy
optical_lumo
gap
homo
lumo
spectral_overlap
delta_homo
delta_lumo
delta_optical_lumo
homo_extrapolated
lumo_extrapolated
gap_extrapolated
optical_lumo_extrapolated
smile: a canonicalized SMILES string representation of the molecule's 2D geometry"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Test set data for the models considering only unique SMILES strings as molecule inputs.",
    "accessURL": "https://data.nrel.gov/system/files/236/1712697052-smiles_test.csv.gz",
    "mediaType": "application/octet-stream",
    "description": "Test set data for the models considering only unique SMILES strings as molecule inputs."
  },
  {
    "@type": "dcat:Distribution",
    "title": "Training set data for the models considering only unique SMILES strings as molecule inputs.",
    "accessURL": "https://data.nrel.gov/system/files/236/1712697052-smiles_train.csv.gz",
    "mediaType": "application/octet-stream",
    "description": "Training set data for the models considering only unique SMILES strings as molecule inputs."
  },
  {
    "@type": "dcat:Distribution",
    "title": "Validation set data for the models considering only unique SMILES strings as molecule inputs.",
    "accessURL": "https://data.nrel.gov/system/files/236/1712697052-smiles_valid.csv.gz",
    "mediaType": "application/octet-stream",
    "description": "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.nrel.gov/submissions/236
license https://creativecommons.org/licenses/by/4.0/
modified 2026-02-26T18:22:21Z
programCode 
                                            [
  "019:008",
  "019:009",
  "019:023"
]
projectNumber
projectTitle
publisher 
                                            {
  "name": "National Renewable Energy Laboratory",
  "@type": "org:Organization"
}
title Organic Photovoltaic (OPV) Database

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