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

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 …

Molecular dynamics (MD) simulations allow insights into complex processes, but accurate
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …

Two-electron oxygen reduction offers a route to H2O2 that is potentially cost-effective and
less energy-intensive than the industrial anthraquinone process. However, the catalytic …

Removal of organic micropollutants from water through advanced oxidation processes
(AOPs) is hampered by the excessive input of energy and/or chemicals as well as the large …

Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …

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 article summarizes technical advances contained in the fifth major release of the Q-
Chem quantum chemistry program package, covering developments since 2015. A …

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 …

DFTB+ is a versatile community developed open source software package offering fast and
efficient methods for carrying out atomistic quantum mechanical simulations. By …