The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and
interfaces bestow them with various exceptional properties. These properties, however, also …

A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …

This work brings the leading accuracy, sample efficiency, and robustness of deep
equivariant neural networks to the extreme computational scale. This is achieved through a …

High-throughput generation of large and consistent ab initio data combined with advanced
machine-learning techniques are enabling the creation of interatomic potentials of near ab …

Chemical and physical properties of complex materials emerge from the collective motions
of the constituent atoms. These motions are in turn determined by a variety of interatomic …

Carbon is one of the most important elements for both industrial applications and
fundamental research, including life, physics, chemistry, materials, and even planetary …

The motion of line defects (dislocations) has been studied for more than 60 years, but the
maximum speed at which they can move is unresolved. Recent models and atomistic …

As the US Department of Energy (DOE) computing facilities began deploying petascale
systems in 2008, DOE was already setting its sights on exascale. In that year, DARPA …

Advances in machine learning (ML) have enabled the development of interatomic potentials
that promise the accuracy of first principles methods and the low-cost, parallel efficiency of …

Liquid carbon remains the source of several unsolved questions related to its structure and
region of thermodynamic stability. Experiments demonstrate a drastic decrease in the …