Gaussian Process Regression for Materials and Molecules

[Image: see text] 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...

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Detalles Bibliográficos
Autores principales: Deringer, Volker L., Bartók, Albert P., Bernstein, Noam, Wilkins, David M., Ceriotti, Michele, Csányi, Gábor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391963/
https://www.ncbi.nlm.nih.gov/pubmed/34398616
http://dx.doi.org/10.1021/acs.chemrev.1c00022

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391963/
https://www.ncbi.nlm.nih.gov/pubmed/34398616
http://dx.doi.org/10.1021/acs.chemrev.1c00022

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