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 …

This paper introduces an active learning approach to the fitting of machine learning
interatomic potentials. Our approach is based on the D-optimality criterion for selecting …

The use of supervised machine learning to develop fast and accurate interatomic potential
models is transforming molecular and materials research by greatly accelerating atomic …

Recent advances in machine-learning interatomic potentials have enabled the efficient
modeling of complex atomistic systems with an accuracy that is comparable to that of …

The molecular dynamics (MD) simulation is a favored method in materials science for
understanding and predicting material properties from atomistic motions. In classical MD …

Machine learning interatomic potentials (MLIPs) enable materials simulations at extended
length and time scales with near-ab initio accuracy. They have broad applications in the …

The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm
change in the development of potential energy surfaces for atomistic simulations. By …

Prediction of material properties from first principles is often a computationally expensive
task. Recently, artificial neural networks and other machine learning approaches have been …

Machine learning interatomic potentials (MLIPs) based on a large data set obtained by
density functional theory calculation have been developed recently. This study gives both …

Nowadays, computer simulations have become a standard tool in essentially all fields of
chemistry, condensed matter physics, and materials science. In order to keep up with state …