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.
Original language | English |
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Pages (from-to) | 10073-10141 |
Number of pages | 69 |
Journal | Chemical Reviews |
Volume | 121 |
Issue number | 16 |
Early online date | 16 Aug 2021 |
DOIs | |
Publication status | Published - 25 Aug 2021 |
Keywords
- aussian process
- regression
- materials
- molecules