期刊
SCIENTIFIC DATA
卷 7, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41597-020-0385-y
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资金
- Deutsche Forschungsgemeinschaft (DFG) through the TUM International Graduate School of Science and Engineering (IGSSE) [GSC 81]
- Academy of Finland [316168, 284621, 305632]
- Magnus Ehrnrooth Foundation
- Finnish Cultural Foundation
- European Union [676580]
- Novel Materials Discovery (NOMAD) Laboratory, a European Center of Excellence
- [316601]
- Academy of Finland (AKA) [316168, 305632, 305632, 316168] Funding Source: Academy of Finland (AKA)
Measurement(s)organic moleculeTechnology Type(s)digital curation center dot spectroscopyFactor Type(s)computational method Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11689347 Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets with realistic molecular geometries and spectral properties are rare. We here supply a diverse benchmark spectroscopy dataset of 61,489 molecules extracted from organic crystals in the Cambridge Structural Database (CSD), denoted OE62. Molecular equilibrium geometries are reported at the Perdew-Burke-Ernzerhof (PBE) level of density functional theory (DFT) including van der Waals corrections for all 62 k molecules. For these geometries, OE62 supplies total energies and orbital eigenvalues at the PBE and the PBE hybrid (PBE0) functional level of DFT for all 62 k molecules in vacuum as well as at the PBE0 level for a subset of 30,876 molecules in (implicit) water. For 5,239 molecules in vacuum, the dataset provides quasiparticle energies computed with many-body perturbation theory in the G(0)W(0) approximation with a PBE0 starting point (denoted GW5000 in analogy to the GW100 benchmark set (M. van Setten et al. J. Chem. Theory Comput. 12, 5076 (2016))).
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