[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124871-en":3,"doc-seo-124871-105":30,"detail-sidebar-cat-0-en-105":91},{"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},124871,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Thermal transport of glasses via machine learning driven simulations - a review and numerical applications","Accessing thermal transport properties of glasses is crucial for industrial production strategies and for devices that rely on glass materials. The work addresses the challenge of modeling glass disorder and local environments, where atomistic simulations are costly and limited by narrow thermodynamic coverage in common potentials. It reviews microscopic heat-transport theory and computational tools centered on machine-learning potentials, applying them to vitreous silica and vitreous silicon, both pure and lithium-intercalated, to compute thermal conductivity across relevant conditions.","arXiv :2402 .06479v1 [ cond-mat .dis-nn] 9 Feb 2024  \nThermal transport of glasses via machine learning driven simulations  \nPaolo Pegolo 1 and Federico Grasselli 2  \n1 SISSA—Scuola Internazionale Superiore di Studi Avanzati, Trieste  \n2 COSMO—Laboratory of Computational Science and Modeling, IMX,  \nÉcole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland  \n(Dated: February 12, 2024)  \nAccessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the chemical and morphological complexity of glasses calls for atomistic simulations where the interatomic potentials are able to capture the variety of local environments, composition, and (dis)order that typically characterize glassy phases. Machine-learning potentials (MLPs) are emerging as a valid alternative to computationally expensive ab initio simulations, inevitably run on very small samples which cannot account for disorder at different scales, as well as to empirical force fields, fast but often reliable only in a narrow portion of the thermodynamic and composition phase diagrams. In this article, we make the point on the use of MLPs to compute the thermal conductivity of glasses, through a review of recent theoretical and computational tools and a series of numerical applications on vitreous silica and vitreous silicon, both pure and intercalated with lithium.  \nThe pursuit of improving the thermal conductivity properties of amorphous solids is central to contemporary materials science and engineering [1] . Glasses, characterized by their lack of crystalline order, possess unique attributes that make them invaluable across a wide range of applications. One of the prominent features of this class of materials is a inherently low thermal conductivity, a result of their disordered structure. This property is useful in various fields, such as aerospace engineering [2, 3], electronics [4], and pharmaceutical industries [5] . In contrast, specific industrial applications demand a nuanced balance in the thermal properties of glasses. For example, nuclear reactors and nuclear weapon decommissioning generate radioactive waste [6] that must be safely stored for exceptionally long time. This waste can be solidified through vitrification, preventing accidental radionuclide release thanks to the amorphous structure of glasses, which provides radiation protection and outstanding chemical durability, thus enabling thousands of years of safe storage [7] . Here, effective heat management is crucial, as high thermal conductivity enhances vitrification efficiency, influencing melt rate and glass homogeneity [8, 9] . Moreover, in long-term storage, elevated heat conductivity rapidly dissipates decaygenerated heat, avoiding issues like high-temperatureassisted crystallization, porosity, and cracks [10] . During the last decades, a huge effort has been put forward to access the structural and thermodynamical properties of the glasses employed for nuclear waste vitrification, mainly (boro)silicates, from both the experimental [8– 11] and the computational sides [12–18] . Nevertheless, a microscopic description of thermal conduction in these materials beyond the celebrated Cahill-Pohl model [19], as a function of temperature and composition, is still missing. The lack of computational studies on thermal conduction is shared by another important application, namely solid-state batteries, where several designs leverage amorphous solid electrolytes [20–24] . Here, the glassy  \nphase is also characterized by a diffusive species (like Li+ or Na+ ions) which poses further challenges for the microscopic simulation of heat transport, since lattice methods cannot be formally applied due to the lack of well defined positions of mechanical equilibrium.  \nMachine-learning (ML) is an increasingly popular tool in m","cbCaijnZ0Xob0yr8","https://ap.wps.com/l/cbCaijnZ0Xob0yr8","pdf",2035422,1,11,"English","en",105,"# Thermal transport in glasses\n## Green-Kubo formulation of thermal conductivity\n## Temperature regimes and scattering mechanisms\n# Materials and methods\n## Machine-learning potentials for amorphous solids\n## Lattice dynamics vs equilibrium molecular dynamics","[{\"question\":\"Why are machine-learning potentials useful for simulating thermal transport in glasses?\",\"answer\":\"They provide accurate surrogate models of expensive ab initio interatomic potentials, enabling atomistic sampling of disordered glass configurations across scales that small ab initio samples cannot cover.\"},{\"question\":\"How is thermal conductivity defined in the described framework?\",\"answer\":\"Thermal conductivity is given by the Green-Kubo linear-response formula using the heat-flux autocorrelation function and system volume, with an isotropy factor.\"},{\"question\":\"What are the key temperature-dependent patterns of thermal conductivity in glasses?\",\"answer\":\"The document highlights three universally recognized regimes, including very low-temperature behavior dominated by quantum tunneling that leads to κ scaling approximately as T^2.\"}]","Thermal transport of glasses via machine learning driven simulations - a review and numerical applications | PDF",1785895146,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"thermal-transport-of-glasses-via-machine-learning-driven-simulations-a-review-and-numerical-applications","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/thermal-transport-of-glasses-via-machine-learning-driven-simulations-a-review-and-numerical-applications/124871/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are machine-learning potentials useful for simulating thermal transport in glasses?","Question",{"text":75,"@type":76},"They provide accurate surrogate models of expensive ab initio interatomic potentials, enabling atomistic sampling of disordered glass configurations across scales that small ab initio samples cannot cover.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is thermal conductivity defined in the described framework?",{"text":80,"@type":76},"Thermal conductivity is given by the Green-Kubo linear-response formula using the heat-flux autocorrelation function and system volume, with an isotropy factor.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the key temperature-dependent patterns of thermal conductivity in glasses?",{"text":84,"@type":76},"The document highlights three universally recognized regimes, including very low-temperature behavior dominated by quantum tunneling that leads to κ scaling approximately as T^2.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]