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 …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

We review progress in neural network (NN)-based methods for the construction of
interatomic potentials from discrete samples (such as ab initio energies) for applications in …

Large-scale atomistic computer simulations of materials rely on interatomic potentials
providing computationally efficient predictions of energy and Newtonian forces. Traditional …

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 …

Quantum-chemistry simulations based on potential energy surfaces of molecules provide
invaluable insight into the physicochemical processes at the atomistic level and yield such …

The success of first-principles electronic-structure calculation for predictive modeling in
chemistry, solid-state physics, and materials science is constrained by the limitations on …

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 …

A lot of progress has been made in recent years in the development of atomistic potentials
using machine learning (ML) techniques. In contrast to most conventional potentials, which …

Large-scale atomistic computer simulations of materials heavily rely on interatomic
potentials predicting the energy and Newtonian forces on atoms. Traditional interatomic …