We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

This review covers a family of atomistic, mostly quantum chemistry (QC) based
semiempirical methods for the fast and reasonably accurate description of large molecules …

Abstract Machine learning potentials have become an important tool for atomistic
simulations in many fields, from chemistry via molecular biology to materials science. Most of …

Advances in machine learned interatomic potentials (MLIPs), such as those using neural
networks, have resulted in short-range models that can infer interaction energies with near …

Abstract Machine-learned force fields combine the accuracy of ab initio methods with the
efficiency of conventional force fields. However, current machine-learned force fields …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …