Gaussian Process Regression for Materials and Molecules,Chemical Reviews

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Gaussian Process Regression for Materials and Molecules
Chemical Reviews ( IF 51.4 ) Pub Date : 2021-08-16 , DOI: 10.1021/acs.chemrev.1c00022
Volker L Deringer ₁ , Albert P Bartók ₂ , Noam Bernstein ₃ , David M Wilkins ₄ , Michele Ceriotti _{5,

6} , Gábor Csányi ₇

Affiliation

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.

中文翻译：

材料和分子的高斯过程回归

我们介绍了计算材料科学和化学中的高斯过程回归 (GPR) 机器学习方法。本综述的重点是原子性质的回归：特别是在高斯近似势（GAP）框架中原子间势或力场的构造；除此之外，我们还讨论了任意标量、矢量和张量的拟合。回顾并批判性地讨论了参考数据生成、表示和回归的方法学方面，以及如何验证数据驱动模型的问题。对化学和材料科学中各种研究问题的应用的调查说明了该领域的快速发展。概述了未来几年该方法的发展愿景。

更新日期：2021-08-25

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