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Gaussian Process Regression for Materials and Molecules
2021
Chemical Reviews
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
doi:10.1021/acs.chemrev.1c00022
pmid:34398616
pmcid:PMC8391963
fatcat:ns54wrx4nzcw3lsztdkxblpeeq