304 citations · 1.1k across the 21 of their papers we have counts for
4 papers · 1 filter
Tensor-reduced atomic density representations
James P. Darby, Dávid P. Kovács, Ilyes Batatia +4
Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials,…
BIP: Boost Invariant Polynomials for Efficient Jet Tagging
Jose M Munoz, Ilyes Batatia, Christoph Ortner
Deep Learning approaches are becoming the go-to methods for data analysis in High Energy Physics (HEP). Nonetheless, most physics-inspired modern architectures are computationally…
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm +2
Creating fast and accurate force fields is a long-standing challenge in computational chemistry and materials science. Recently, several equivariant message passing neural networks…
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács +6
The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic…