Distilling atomistic foundation models! ⚗️🧪🤖 In this #compchem preprint, we describe a general (“architecture-agnostic”) approach to creating fast, application-specific MLIP models via synthetic data – led jointly by @jla-gardner.bsky.social & @dft-dutoit.bsky.social arxiv.org/abs/2506.10956
Distillation of atomistic foundation models across architectures and chemical domains
Machine-learned interatomic potentials have transformed computational research in the physical sciences. Recent atomistic `foundation' models have changed the field yet again: trained on many differen...
arxiv.org