The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

• Published in: Scientific data Vol. 7; no. 1; p. 134
• Main Authors: Smith, Justin S., Zubatyuk, Roman, Nebgen, Benjamin, Lubbers, Nicholas, Barros, Kipton, Roitberg, Adrian E., Isayev, Olexandr, Tretiak, Sergei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.05.2020; Nature Publishing Group
• Subjects: 639/638/563/979; 639/638/563/980; 639/638/630; Chemical elements; cheminformatics; Computer applications; Data Descriptor; Datasets; density functional theory; Energy charge; Humanities and Social Sciences; INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Learning algorithms; Machine learning; method development; Molecular modelling; multidisciplinary; Quantum physics; Science; Science (multidisciplinary)
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-020-0473-z

Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry. Measurement(s) Quantum Mechanics • energy • force • multipole moment • atomic charge Technology Type(s) computational modeling technique Factor Type(s) atom Sample Characteristic - Environment organic molecule Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12046440