Gaussian Process Regression for Materials and Molecules

We provide an introduction to Gaussian process regression (GPR) machinelearning 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.

Automated summary

This paper introduces the application of Gaussian process regression machine learning in computational materials science and chemistry, focusing on the regression of atomistic properties, including the construction of interatomic potentials, force fields, and fitting of various quantities. Methodological aspects of reference data generation, representation, regression, and model validation are reviewed, along with a survey of applications in chemistry and materials science and an outline of future development vision.