%0 Journal Article
%T Finding defects in glasses through machine learning
%+ Systèmes Désordonnés et Applications
%+ Columbia University [New York]
%+ Laboratoire Charles Coulomb (L2C)
%+ Laboratoire de physique de l'ENS - ENS Paris (LPENS)
%A Ciarella, Simone
%A Khomenko, Dmytro
%A Berthier, Ludovic
%A Mocanu, Felix
%A Reichman, David
%A Scalliet, Camille
%A Zamponi, Francesco
%< avec comité de lecture
%@ 2041-1723
%J Nature Communications
%I Nature Publishing Group
%V 14
%N 1
%P 4229
%8 2023-12
%D 2023
%Z 2212.05582
%R 10.1038/s41467-023-39948-7
%Z Physics [physics]Journal articles
%X 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.
%G English
%2 https://cnrs.hal.science/hal-04165248/document
%2 https://cnrs.hal.science/hal-04165248/file/2212.05582.pdf
%L hal-04165248
%U https://cnrs.hal.science/hal-04165248
%~ ENS-PARIS
%~ CNRS
%~ OPENAIRE
%~ L2C
%~ PSL
%~ UNIV-MONTPELLIER
%~ SORBONNE-UNIVERSITE
%~ SORBONNE-UNIV
%~ LPENS
%~ UNIV-PARIS
%~ UNIVERSITE-PARIS
%~ UP-SCIENCES
%~ ENS-PSL
%~ SU-TI
%~ ANR
%~ ALLIANCE-SU
%~ UM-2015-2021
%~ UM-EPE