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

Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

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 …

Message passing neural networks have become a method of choice for learning on graphs,
in particular the prediction of chemical properties and the acceleration of molecular …

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 …

The subject of this paper is the technology (the'how') of constructing machine-learning
interatomic potentials, rather than science (the'what'and'why') of atomistic simulations using …

We present our latest advancements of machine-learned potentials (MLPs) based on the
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …

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 …

The expressive power of Graph Neural Networks (GNNs) has been studied extensively
through the Weisfeiler-Leman (WL) graph isomorphism test. However, standard GNNs and …