Abstract Machine learning (ML) enables the development of interatomic potentials with the
accuracy of first principles methods while retaining the speed and parallel efficiency of …

So far, it has been a challenge for existing interatomic potentials to accurately describe a
wide range of physical properties and maintain reasonable efficiency. In this work, we …

We present a study on the transport and material properties of aluminum spanning from
ambient to warm dense matter conditions using a machine-learned interatomic potential (ML …

The study of ultrafast photoinduced dynamics of adsorbates on metal surfaces requires
thorough investigation of laser-excited electrons and, in many cases, the highly excited …

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 …

Over the past two decades, the US-DOE has funded multiple projects that rely on high-
performance computing and exascale computing platforms to accelerate scientific …

This study utilizes first principles calculations of density functional theory and Spectral
Neighbor Analysis Potential (SNAP) machine learning potential to investigate the influence …

During the operation of nuclear fusion reactors, plasma-facing components lining the reactor
vessel are continually bombarded by plasma species. The penetration and subsequent …

Accurate simulations of materials at long-time and large-length scales have increasingly
been enabled by Machine-learned Interatomic Potentials (MLIPs). There have been …