Machine learning (ML) models for molecules and materials commonly rely on a
decomposition of the global target quantity into local, atom-centered contributions. This …

Over the past decade, interatomic potentials based on machine learning (ML) techniques
have become an indispensable tool in the atomic-scale modeling of materials. Trained on …

We develop a framework for on-the-fly machine learned force field (MLFF) molecular
dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF …

We present a study of the principal deuterium Hugoniot for pressures up to 150 GPa, using
machine learning potentials (MLPs) trained with quantum Monte Carlo (QMC) energies …

We present a framework for computing the shock Hugoniot using on-the-fly machine learned
force field (MLFF) molecular dynamics simulations. In particular, we employ an MLFF model …

The electronic density of states (DOS) provides information regarding the distribution of
electronic energy levels in a material, and can be used to approximate its optical and …

We present the electronic moment tensor potentials (eMTPs), a class of machine-learning
interatomic models and a generalization of the classical MTPs, reproducing both the …

We introduce machine learning (ML) models that predict the electronic structure of materials
across a wide temperature range. Our models employ neural networks and are trained on …

The electronic density of states (DOS) provides information regarding the distribution of
electronic energy levels in a material, and can be used to approximate its optical and …

Electronic structure simulations allow researchers to compute fundamental properties of
materials without the need for experimentation. As such, they routinely aid in propelling …