Physics-Informed Active Learning for Accelerating Quantum Chemical Simulations
Yi-Fan Hou,
Lina Zhang,
Quanhao Zhang
et al.
Abstract:Quantum
chemical simulations can be greatly accelerated by constructing
machine learning potentials, which is often done using active learning
(AL). The usefulness of the constructed potentials is often limited
by the high effort required and their insufficient robustness in the
simulations. Here, we introduce the end-to-end AL for constructing
robust data-efficient potentials with affordable investment of time
and resources and minimum human interference. Our AL protocol is based
on the physics-informed sampl… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.