Preprint
Oct 2026
Atoms that Matter: Largest Materials Datasets Are 99% Redundant, and Local Chemistry Can Guide the Required 1%
General-purpose machine-learning force fields (MLFFs) for materials are now trained on the order of $100$ million structures, assuming transferability requires such scale. We test this premise on OMat24, the largest available materials dataset, by training MACE models on subsets of $6.5$ million atoms, less than $1\%$...
Evgeny Moerman, Igor Poltavsky, Anton Charkin-Gorbulin et al.
· 0 citations