Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation

Abstract

Machine learning is becoming increasingly more important in the field of force field development. Never has it been more vital to have chemically accurate machine learning potentials because force fields become more sophisticated and their applications expand. In this study a method for developing chemically accurate Gaussian process regression models is demonstrated for an increasingly complex set of molecules. This work is an extension to previous work showing the progression of the active learning technique in producing more accurate models in much less CPU time than ever before. The per-atom active learning approach has unlocked the potential to generate chemically accurate models for molecules such as peptide-capped glycine.

Keywords: FFLUX; Gaussian process regression; IQA; QTAIM; kriging; machine learning; particle swarm optimization; quantum chemical topology.