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 …

The atomic cluster expansion (ACE) was proposed recently as a new class of data-driven
interatomic potentials with a formally complete basis set. Since the development of any …

Deep learning has emerged as a promising paradigm to give access to highly accurate
predictions of molecular and material properties. A common short-coming shared by current …

Machine learning potentials have become an essential tool for atomistic simulations,
yielding results close to ab initio simulations at a fraction of computational cost. With recent …

This work examines challenges associated with the accuracy of machine-learned force
fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we …

The increased time-and length-scale of classical molecular dynamics simulations have led
to raw data flows surpassing storage capacities, necessitating on-the-fly integration of …

Supported metal nanoparticle (NP) catalysts are vital for the sustainable production of
chemicals, but their design and implementation are limited by the ability to identify and …

Thermodynamic phase stability of three elemental boron allotropes, ie, α-B, β-B, and γ-B,
was investigated using a Bayesian interatomic potential trained via a sparse Gaussian …

Coarse graining techniques play an essential role in accelerating molecular simulations of
systems with large length and time scales. Theoretically grounded bottom-up models are …

The activity of metal catalysts depends sensitively on dynamic structural changes that occur
during operating conditions. The mechanistic understanding underlying such …