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 …

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 …

In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

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 …

Since the experimental characterization of the low-pressure region of water's phase diagram
in the early 1900s, scientists have been on a quest to understand the thermodynamic …

Structures and processes at water/metal interfaces play an important technological role in
electrochemical energy conversion and storage, photoconversion, sensors, and corrosion …

In recent years, machine learning (ML) methods have become increasingly popular in
computational chemistry. After being trained on appropriate ab initio reference data, these …

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 …

Modern simulation techniques have reached a level of maturity which allows a wide range of
problems in chemistry and materials science to be addressed. Unfortunately, the application …