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 …

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 …

An accurate determination of the potential energy is the crucial step in computer simulations
of chemical processes, but using electronic structure methods on-the-fly in molecular …

In the past two decades, machine learning potentials (MLPs) have reached a level of
maturity that now enables applications to large-scale atomistic simulations of a wide range …

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 …

The development of interatomic potentials employing artificial neural networks has seen
tremendous progress in recent years. While until recently the applicability of neural network …

The accuracy of the results obtained in molecular dynamics or Monte Carlo simulations
crucially depends on a reliable description of the atomic interactions. A large variety of …

Neural networks and other machine learning approaches have been successfully used to
accurately represent atomic interaction potentials derived from computationally demanding …

Over the past years high-dimensional neural network potentials (HDNNPs), fitted to
accurately reproduce ab initio potential energy surfaces, have become a powerful tool in …

The potential energy of molecular species and their conformers can be computed with a
wide range of computational chemistry methods, from molecular mechanics to ab initio …