Gaussian Process Regression for Materials and Molecules.

TLDR: In this paper, the authors provide an introduction to Gaussian process regression (GPR) machine learning methods in computational materials science and chemistry, focusing on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian approximation potential (GAP) framework.

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.