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 …

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

Best practices in machine learning for chemistry | Nature Chemistry Skip to main content
Thank you for visiting nature.com. You are using a browser version with limited support for …

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 …

The design of heterogeneous catalysts is necessarily surface‐focused, generally achieved
via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure …

Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

Large-scale atomistic computer simulations of materials rely on interatomic potentials
providing computationally efficient predictions of energy and Newtonian forces. Traditional …

As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …