Gaussian Process Regression for Materials and Molecules.
Bartók, Albert P
American Chemical Society
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Deringer, V. L., Bartók, A. P., Bernstein, N., Wilkins, D. M., Ceriotti, M., & Csányi, G. (2021). Gaussian Process Regression for Materials and Molecules.. Chemical reviews, 121 (16), 10073-10141. https://doi.org/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 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.
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External DOI: https://doi.org/10.1021/acs.chemrev.1c00022
This record's URL: https://www.repository.cam.ac.uk/handle/1810/325110
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