Ontology highlight
ABSTRACT:
SUBMITTER: Kulichenko M
PROVIDER: S-EPMC10766548 | biostudies-literature | 2023 Mar
REPOSITORIES: biostudies-literature
Kulichenko Maksim M Barros Kipton K Lubbers Nicholas N Li Ying Wai YW Messerly Richard R Tretiak Sergei S Smith Justin S JS Nebgen Benjamin B
Nature computational science 20230306 3
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If the uncertainty estimate passes a certain threshold, then the configuration is included in the data set. Here we develop a strategy to more ra ...[more]