Finding defects in glasses through machine learning.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
15 Jul 2023
15 Jul 2023
Historique:
received:
08
02
2023
accepted:
20
06
2023
medline:
17
7
2023
pubmed:
16
7
2023
entrez:
15
7
2023
Statut:
epublish
Résumé
Structural defects control the kinetic, thermodynamic and mechanical properties of glasses. For instance, rare quantum tunneling two-level systems (TLS) govern the physics of glasses at very low temperature. Due to their extremely low density, it is very hard to directly identify them in computer simulations. We introduce a machine learning approach to efficiently explore the potential energy landscape of glass models and identify desired classes of defects. We focus in particular on TLS and we design an algorithm that is able to rapidly predict the quantum splitting between any two amorphous configurations produced by classical simulations. This in turn allows us to shift the computational effort towards the collection and identification of a larger number of TLS, rather than the useless characterization of non-tunneling defects which are much more abundant. Finally, we interpret our machine learning model to understand how TLS are identified and characterized, thus giving direct physical insight into their microscopic nature.
Identifiants
pubmed: 37454138
doi: 10.1038/s41467-023-39948-7
pii: 10.1038/s41467-023-39948-7
pmc: PMC10349890
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4229Informations de copyright
© 2023. The Author(s).
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