ArticleNature communications2023
Finding defects in glasses through machine learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Interface Engineering in MOF-Derived NiOOH/FeOOH Heterostructures: Boosting OER Performance via Lattice Oxygen Redox Regulation and AEM/LOM Synergy.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Hedgehog topological defects in 3D amorphous solids.Nature communications · 2025Article
- Structural origin of relaxation in dense colloidal suspensions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Local vs. cooperative: Unraveling glass transition mechanisms with SEER.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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