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 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 activation of O2 on metal surfaces is a critical process for heterogeneous catalysis and
materials oxidation. Fundamental studies of well-defined metal surfaces using a variety of …

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 …

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

We review the state-of-the-art in the theory of dissociative chemisorption (DC) of small gas
phase molecules on metal surfaces, which is important to modeling heterogeneous catalysis …

Dissociative chemisorption is the initial and often rate-limiting step in many heterogeneous
processes. As a result, an in-depth understanding of the reaction dynamics of such …

The permutation invariant polynomial-neural network (PIP-NN) method for constructing
highly accurate potential energy surfaces (PESs) for gas phase molecules is extended to …

Conspectus Energy dissipation at surfaces and interfaces is mediated by excitation of
elementary processes, including phonons and electronic excitation, once external energy is …

The oxygen species on Ag catalysts and reaction mechanisms for ethylene epoxidation and
ethylene combustion continue to be debated in the literature despite decades of …