Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

Large-scale simulations with complex electron interactions remain one of the greatest
challenges for atomistic modelling. Although classical force fields often fail to describe the …

Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E (3)-
equivariant neural network approach for learning interatomic potentials from ab-initio …

Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …

Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …

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 …

In the past two decades, machine learning potentials (MLPs) have reached a level of
maturity that now enables applications to large-scale atomistic simulations of a wide range …

Recent years have seen vast progress in the development of machine learned force fields
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …

Advances in machine learned interatomic potentials (MLIPs), such as those using neural
networks, have resulted in short-range models that can infer interaction energies with near …