Zero Shot Molecular Generation via Similarity Kernels

2 code implementations • 13 Feb 2024 • Rokas Elijošius, Fabian Zills, Ilyes Batatia, Sam Walton Norwood, Dávid Péter Kovács, Christian Holm, Gábor Csányi

Using insights from the trained model, we present Similarity-based Molecular Generation (SiMGen), a new method for zero shot molecular generation.

Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials

no code implementations • 9 Oct 2022 • Cas van der Oord, Matthias Sachs, Dávid Péter Kovács, Christoph Ortner, Gábor Csányi

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 properties with experimental accuracy.

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

2 code implementations • 15 Jun 2022 • Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, Gábor Csányi

In particular, we show that using four-body messages reduces the required number of message passing iterations to just two, resulting in a fast and highly parallelizable model, reaching or exceeding state-of-the-art accuracy on the rMD17, 3BPA, and AcAc benchmark tasks.

Computational chemistry

The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

2 code implementations • 13 May 2022 • Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi

The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures.