“Gaussian Process Regression for Materials and Molecules” (2021), by Volker L. Deringer et al. Materials Science research paper. Original abstract below, unchanged. No free PDF was found.

Gaussian Process Regression for Materials and Molecules

Volker L. Deringer, Albert P. Bartók, Noam Bernstein et al. · August 16, 2021 · Materials Science

Abstract

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.

Source page DOI: 10.1021/acs.chemrev.1c00022