Gaussian Process Regression for Materials and Molecules

• Published in: Chemical reviews Vol. 121; no. 16; pp. 10073 - 10141
• Main Authors: Deringer, Volker L, Bartók, Albert P, Bernstein, Noam, Wilkins, David M, Ceriotti, Michele, Csányi, Gábor
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 25-08-2021
• Subjects: Gaussian process; Machine learning; Materials science; Questions; Regression; Review
• ISSN: 0009-2665; 1520-6890
• DOI: 10.1021/acs.chemrev.1c00022

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.