Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
Type
Publication
npj Computational Materials
In this paper we combine uncertainty-biased molecular dynamics with active learning to show how we can learn machine learning interatomic potential (MLIP) models that are more robust to predictions on extrapolative regions.
Federico Errica
Senior Research Scientist
My research interests include distributed robotics, mobile computing and programmable matter.