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 …

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 …

Molecular dynamics simulation is an important tool in computational materials science and
chemistry, and in the past decade it has been revolutionized by machine learning. This rapid …

We develop a neuroevolution-potential (NEP) framework for generating neural network-
based machine-learning potentials. They are trained using an evolutionary strategy for …

Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …

We demonstrate that fast and accurate linear force fields can be built for molecules using the
atomic cluster expansion (ACE) framework. The ACE models parametrize the potential …

The atomic cluster expansion is a general polynomial expansion of the atomic energy in
multi-atom basis functions. Here we implement the atomic cluster expansion in the …

Many-body descriptors are widely used to represent atomic environments in the construction
of machine-learned interatomic potentials and more broadly for fitting, classification, and …