304 citations · 775 across the 7 of their papers we have counts for
5 papers · 1 filter
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials
Cas van der Oord, Matthias Sachs, Dávid Péter Kovács +2
Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic…
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,…
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…
Cooling Down FaaS: Towards Getting Rid of Warm Starts
Dániel Géhberger, Dávid Kovács
Serverless execution and most notably the Function as a Service (FaaS) model got quite some attention during the recent years. As of today, all commercial and open source implement…
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…