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

Fiber-reinforced polymer (FRP) composites have gained widespread applications in many
engineering fields, making it imperative to study long-term performance under service …

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

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 …

Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and
forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC …

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 …

As the sophistication of machine learning force fields (MLFF) increases to match the
complexity of extended molecules and materials, so does the need for tools to properly …

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 …

The value of uncertainty quantification on predictions for trained neural networks (NNs) on
quantum chemical reference data is quantitatively explored. For this, the architecture of the …

Machine learning potentials enable molecular dynamics simulations to exceed the size and
time scales that can be accessed by first-principles methods like density functional theory …