activity
20222024
most citedMACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

304 citations · 775 across the 7 of their papers we have counts for

collaborators

7 papers

physics.chem-ph2024★ 249 cited

A foundation model for atomistic materials chemistry

Ilyes Batatia, Philipp Benner, Yuan Chiang +85

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…

physics.chem-ph2023★ 68 cited

MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Dávid Péter Kovács, J. Harry Moore, Nicholas J. Browning +8

Classical empirical force fields have dominated biomolecular simulation for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dy…

physics.comp-ph2022★ 9 cited

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…

physics.chem-ph2022★ 40 cited

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,…

stat.ML2022★ 304 cited

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…

cs.DC2022★ 2 cited

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…

stat.ML2022★ 103 cited

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…