The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …

Matter at extreme temperatures and pressures—commonly known as warm dense matter
(WDM)—is ubiquitous throughout our Universe and occurs in astrophysical objects such as …

We present an atomic cluster expansion (ACE) for carbon that improves over available
classical and machine learning potentials. The ACE is parametrized from an exhaustive set …

Data science and material informatics are gaining traction in alloy design. This is due to
increasing infrastructure, computational capabilities and established open-source …

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

Reactive force fields for molecular dynamics have enabled a wide range of studies in
numerous material classes. These force fields are computationally inexpensive compared …

The global energy optimization problem is an acute and important problem in chemistry. It is
crucial to know the geometry of the lowest energy isomer (global minimum, GM) of a given …

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 …

Carbon phosphide is a newly discovered two-dimensional semiconductor material which
wrinkles and has a significant carrier mobility. Due to lack an accurate force field, the use of …

Diamond possesses exceptional physical properties due to its remarkably strong carbon–
carbon bonding, leading to significant resilience to structural transformations at very high …