[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117839-en":3,"doc-seo-117839-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117839,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Finding defects in glasses through machine learning","Structural defects in glasses govern kinetic, thermodynamic, and mechanical properties. At very low temperatures, rare quantum tunneling two-level systems (TLS) play a central role, but their extremely low density makes direct identification in simulations difficult. A machine learning approach is introduced to efficiently explore the potential energy landscape of glass models and rapidly predict quantum splitting between any two amorphous configurations. The method shifts effort toward collecting TLS, not abundant non-tunneling defects, and the model is interpreted to extract microscopic physical insight into how TLS are identified and characterized.","arXiv :2212 .05582v4 [ cond-mat .dis-nn] 16 May 2023  \nFinding defects in glasses through machine learning  \nSimone Ciarella, 1, ∗ Dmytro Khomenko,2, 3, ∗ Ludovic Berthier,4, 5 Felix C.  \nMocanu, 1 David R. Reichman,2 Camille Scalliet,6 and Francesco Zamponi 1  \n1 Laboratoire de Physique de l'cole Normale Sup􀀓erieure, ENS, Universit􀀓e PSL,  \nCNRS, Sorbonne Universit􀀓e, Universit􀀓e de Paris, 75005 Paris, France  \n2 Department of Chemistry, Columbia University, 3000 Broadway, New York, NY 10027, USA  \n3 Dipartimento di Fisica, Sapienza Universit􀀒a di Roma, P. le A. Moro 2, I-00185, Rome, Italy  \n4 Yusuf Hamied Department of Chemistry, University of Cambridge,  \nLens􀀌eld Road, Cambridge CB2 1EW, United Kingdom  \n5 Laboratoire Charles Coulomb (L2C), Universit􀀓e de Montpellier, CNRS, 34095 Montpellier, France  \n6 DAMTP, Centre for Mathematical Sciences, University of Cambridge,  \nWilberforce Road, Cambridge CB3 0WA, United Kingdom  \n(Dated: May 17, 2023)  \nStructural 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 e􀀎ciently 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 con􀀌gurations produced by classical simulations. This in turn allows us to shift the computational e􀀋ort towards the collection and identi􀀌cation 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 identi􀀌ed and characterized, thus giving direct physical insight into their microscopic nature.  \nI. INTRODUCTION  \nWhen a glass-forming liquid is cooled rapidly, its viscosity increases dramatically and it eventually transforms into an amorphous solid, called a glass, whose physical properties are profoundly di􀀋erent from those of ordered crystalline solids [1] . At even lower temperature, around 1 K, the speci􀀌c heat of a disordered solid is much larger than that of its crystalline counterpart as it scales linearly rather than cubically with temperature. Similarly, the temperature evolution of the thermal conductivity in glasses is quadratic, rather than cubic [2{11] . A theoretical framework rationalizing such anomalous behavior was provided by Anderson, Halperin and Varma [12] and by Phillips [13, 14] . They argued that the energy landscape of amorphous solids contains many nearlydegenerate minima, connected by the localized motion of a few atoms, that can act as tunneling defects, called two-level systems (TLS) [15{19] . Since then, localized structural defects have been understood to play a crucial role in many other glass properties [20] . Understanding the microscopic origin of such localized defects and how to control their density and physical properties is a major goal not only to improve our fundamental understanding of amorphous solids, but also for technological applications, such as optimizing the performance of certain quantum devices [21, 22] .  \n∗ These authors contributed equally.  \nemail: [simone.ciarella@ens.fr](simone.ciarella@ens.fr), [dmytro.khomenko@uniroma1.it](dmytro.khomenko@uniroma1.it)  \nThe development of particle-swap computer algorithms [23, 24] has allowed the creation of computer glasses at unprecedentedly low temperatures. Combined with potential energy landscape exploration algorithms [25{34], this provides a powerful method to investigate the nature of defects in materials prepared under conditions comparable to experimental studies [35, 36] . These tools have enabled direct numerical observation of TLS [35, 37], con􀀌r","cbCaisLcD7pOz2QU","https://ap.wps.com/l/cbCaisLcD7pOz2QU","pdf",9458184,1,23,"English","en",105,"# Introduction\n## Structural defects and TLS in glasses\n## Potential energy landscape exploration and computational challenges\n## Proposed machine-learning strategy for TLS identification","[{\"question\":\"Why are TLS difficult to identify directly in computer simulations?\",\"answer\":\"TLS are extremely rare because of their very low density, so direct detection through exhaustive landscape methods is computationally challenging.\"},{\"question\":\"What does the proposed machine learning approach do?\",\"answer\":\"It explores the potential energy landscape of glass models and rapidly predicts the quantum splitting between any two amorphous configurations produced by classical simulations.\"},{\"question\":\"How does the method improve computational efficiency compared with prior approaches?\",\"answer\":\"Instead of evaluating only pairs of inherent structures explored consecutively in dynamics, it considers all IS pairs and focuses computational effort on collecting and identifying TLS rather than characterizing much more abundant non-tunneling defects.\"}]","Finding defects in glasses through machine learning | 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are TLS difficult to identify directly in computer simulations?","Question",{"text":76,"@type":77},"TLS are extremely rare because of their very low density, so direct detection through exhaustive landscape methods is computationally challenging.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What does the proposed machine learning approach do?",{"text":81,"@type":77},"It explores the potential energy landscape of glass models and rapidly predicts the quantum splitting between any two amorphous configurations produced by classical simulations.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the method improve computational efficiency compared with prior approaches?",{"text":85,"@type":77},"Instead of evaluating only pairs of inherent structures explored consecutively in dynamics, it considers all IS pairs and focuses computational effort on collecting and identifying TLS rather than characterizing much more abundant non-tunneling 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