In the past two decades, machine learning potentials (MLPs) have driven significant
developments in chemical, biological, and material sciences. The construction and training …

Artificial Neural Networks (NN) are already heavily involved in methods and applications for
frequent tasks in the field of computational chemistry such as representation of potential …

Abstract Machine learning potentials are an important tool for molecular simulation, but their
development is held back by a shortage of high quality datasets to train them on. We …

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 …

Multi-component alloys have demonstrated excellent performance in various applications,
but the vast range of possible compositions and microstructures makes it challenging to …

In recent years, significant progress has been made in the development of machine learning
potentials (MLPs) for atomistic simulations with applications in many fields from chemistry to …

MXenes represent one of the largest classes of 2D materials with promising applications in
many fields and their properties are tunable by altering the surface group composition …

The dissociation rate (k off) associated with ligand unbinding events from proteins is a
parameter of fundamental importance in drug design. Here we review recent major …

Neural networks (NNs) often assign high confidence to their predictions, even for points far
out of distribution, making uncertainty quantification (UQ) a challenge. When they are …

We report on an extensive study of the viscosity of liquid water at near-ambient conditions,
performed within the Green-Kubo theory of linear response and equilibrium ab initio …