129 citations · 141 across the 8 of their papers we have counts for
3 papers · 1 filter
Distillation of atomistic foundation models across architectures and chemical domains
John L. A. Gardner, Daniel F. Thomas du Toit, Chiheb Ben Mahmoud +8
Machine-learned interatomic potentials have transformed computational research in the physical sciences. Recent atomistic `foundation' models have changed the field yet again: trai…
An automated framework for exploring and learning potential-energy surfaces
Yuanbin Liu, Joe D. Morrow, Christina Ertural +6
Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing mach…
Synthetic pre-training for neural-network interatomic potentials
John L. A. Gardner, Kathryn T. Baker, Volker L. Deringer
Machine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity…