2 papers
physics.chem-ph2025
Global properties of the energy landscape: a testing and training arena for machine learned potentials
Vlad CÄrare, Fabian L. Thiemann, Joe Morrow +3
Machine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics -- critical for reaction…
cs.LG2025
Refining embeddings with fill-tuning: data-efficient generalised performance improvements for materials foundation models
Matthew P. Wilson, Edward O. Pyzer-Knapp, Nicolas Galichet +1
Pretrained foundation models learn embeddings that can be used for a wide range of downstream tasks. These embeddings optimise general performance, and if insufficiently accurate a…