Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials.

ABSTRACT: Atomistic simulations driven by machine-learned interatomic potentials (MLIPs) are a cost-effective alternative to ab initio molecular dynamics (AIMD). Yet, their broad applicability in reaction modelling remains hindered, in part, by the need for large training datasets that adequately sample the relevant potential energy surface, including high-energy transition state (TS) regions. To optimise dataset generation and extend the use of MLIPs for reaction modelling, we present a data-efficient and fully automated workflow for MLIP training that requires only a small number (typically five to ten) of initial configurations and no prior knowledge of the TS. The approach combines automated active learning with well-tempered metadynamics to iteratively and selectively explore chemically

SUBMITTER: Vitartas V 

PROVIDER: S-EPMC12642453 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature