Ontology highlight
ABSTRACT:
SUBMITTER: Unke OT
PROVIDER: S-EPMC8671403 | biostudies-literature | 2021 Dec
REPOSITORIES: biostudies-literature
Unke Oliver T OT Chmiela Stefan S Gastegger Michael M Schütt Kristof T KT Sauceda Huziel E HE Müller Klaus-Robert KR
Nature communications 20211214 1
Machine-learned force fields combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current machine-learned force fields typically ignore electronic degrees of freedom, such as the total charge or spin state, and assume chemical locality, which is problematic when molecules have inconsistent electronic states, or when nonlocal effects play a significant role. This work introduces SpookyNet, a deep neural network for constructing machine-learned force ...[more]