Ontology highlight
ABSTRACT:
SUBMITTER: Chmiela S
PROVIDER: S-EPMC9833674 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Chmiela Stefan S Vassilev-Galindo Valentin V Unke Oliver T OT Kabylda Adil A Sauceda Huziel E HE Tkatchenko Alexandre A Müller Klaus-Robert KR
Science advances 20230111 2
Global machine learning force fields, with the capacity to capture collective interactions in molecular systems, now scale up to a few dozen atoms due to considerable growth of model complexity with system size. For larger molecules, locality assumptions are introduced, with the consequence that nonlocal interactions are not described. Here, we develop an exact iterative approach to train global symmetric gradient domain machine learning (sGDML) force fields (FFs) for several hundred atoms, with ...[more]