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 poi

SUBMITTER: Smith JS 

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

REPOSITORIES: biostudies-literature