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

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

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 …

Molecular dynamics (MD) simulation techniques are widely used for various natural science
applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab …

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 …

Simulation techniques based on accurate and efficient representations of potential energy
surfaces are urgently needed for the understanding of complex systems such as solid–liquid …

In the past two and a half decades machine learning potentials have evolved from a special
purpose solution to a broadly applicable tool for large-scale atomistic simulations. By …

Operando characterization plays an important role in revealing the structure–property
relationships of organic mixed ionic/electronic conductors (OMIECs), enabling the direct …

Machine learning models for the potential energy of multi-atomic systems, such as the deep
potential (DP) model, make molecular simulations with the accuracy of quantum mechanical …

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 …