SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
14 Dec 2021
14 Dec 2021
Historique:
received:
12
07
2021
accepted:
16
11
2021
entrez:
15
12
2021
pubmed:
16
12
2021
medline:
16
12
2021
Statut:
epublish
Résumé
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 fields with explicit treatment of electronic degrees of freedom and nonlocality, modeled via self-attention in a transformer architecture. Chemically meaningful inductive biases and analytical corrections built into the network architecture allow it to properly model physical limits. SpookyNet improves upon the current state-of-the-art (or achieves similar performance) on popular quantum chemistry data sets. Notably, it is able to generalize across chemical and conformational space and can leverage the learned chemical insights, e.g. by predicting unknown spin states, thus helping to close a further important remaining gap for today's machine learning models in quantum chemistry.
Identifiants
pubmed: 34907176
doi: 10.1038/s41467-021-27504-0
pii: 10.1038/s41467-021-27504-0
pmc: PMC8671403
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7273Subventions
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
ID : P2BSP2_188147
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : Math+, EXC 2046/1, Project ID 390685689
Informations de copyright
© 2021. The Author(s).
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