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

The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …

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 …

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 …

Symmetry adaptation is crucial in representing a permutationally invariant potential energy
surface (PES). Due to the rapid increase in computational time with respect to the molecular …

The mathematical representation of large data sets of electronic energies has seen
substantial progress in the past 10 years. The so-called Permutationally Invariant …

Three active learning schemes are used to generate training data for Gaussian process
interpolation of intermolecular potential energy surfaces. These schemes aim to achieve the …

Machine learning (ML) is having an increasing impact on the physical sciences,
engineering, and technology and its integration into molecular simulation frameworks holds …