In chemistry and physics, machine learning (ML) methods promise transformative impacts by
advancing modeling and improving our understanding of complex molecules and materials …

Pressure, a fundamental thermodynamic variable, can generate two essential effects on
materials. First, pressure can create new high-pressure phases via modification of the …

We demonstrate that fast and accurate linear force fields can be built for molecules using the
atomic cluster expansion (ACE) framework. The ACE models parametrize the potential …

Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes
and materials. ML-based force fields, trained on large data sets of high-quality electron …

Neural network (NN) interatomic potentials provide fast prediction of potential energy
surfaces, closely matching the accuracy of the electronic structure methods used to produce …

Graph neural networks (GNNs) have been shown to be extremely flexible and accurate in
predicting the physical properties of molecules and crystals. However, traditional invariant …

There has been great progress in developing methods for machine-learned potential energy
surfaces. There have also been important assessments of these methods by comparing so …

The affordable, accurate, and generalizable prediction of spectroscopic observables plays a
key role in the analysis of increasingly complex experiments. In this article, we develop and …

An accurate, generalizable, and transferable force field plays a crucial role in the molecular
dynamics simulations of organic polymers and biomolecules. Conventional empirical force …

Computational methods used for predicting the crystal structures of organic compounds are
mature enough to be routinely used with many rigid and semi-rigid organic molecules. The …