High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Deep learning has the potential to accelerate atomistic simulations, but existing models
suffer from a lack of robustness, sample efficiency, and accuracy. Simon Batzner, Albert …

Molecular dynamics (MD) simulation techniques are widely used for various natural science
applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab …

The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Modeling the energy and forces of atomic systems is a fundamental problem in
computational chemistry with the potential to help address many of the world's most pressing …

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 …

Point clouds are versatile representations of 3D objects and have found widespread
application in science and engineering. Many successful deep-learning models have been …

Machine learning (ML) is increasingly becoming a common tool in computational chemistry.
At the same time, the rapid development of ML methods requires a flexible software …

Graph neural networks (GNNs) have recently been used to learn the representations of
crystal structures through an end-to-end data-driven approach. However, a systematic top …