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

ABSTRACT: 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.

SUBMITTER: Smith JS

PROVIDER: S-EPMC7195467 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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

Smith Justin S JS Zubatyuk Roman R Nebgen Benjamin B Lubbers Nicholas N Barros Kipton K Roitberg Adrian E AE Isayev Olexandr O Tretiak Sergei S

Scientific data 20200501 1

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 ...[more]

PMID: 32358545

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Dataset Information

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

Publications

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

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