Thermal transport of glasses via machine learning driven simulations
Accessing 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 glasse...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Materials |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2024.1369034/full |
| _version_ | 1797272617554542592 |
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| author | Paolo Pegolo Federico Grasselli |
| author_facet | Paolo Pegolo Federico Grasselli |
| author_sort | Paolo Pegolo |
| collection | DOAJ |
| description | Accessing 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. |
| first_indexed | 2024-03-07T14:31:56Z |
| format | Article |
| id | doaj.art-57b29a05e5ce408cb029d673ca0ec163 |
| institution | Directory Open Access Journal |
| issn | 2296-8016 |
| language | English |
| last_indexed | 2024-03-07T14:31:56Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj.art-57b29a05e5ce408cb029d673ca0ec1632024-03-06T04:21:53ZengFrontiers Media S.A.Frontiers in Materials2296-80162024-03-011110.3389/fmats.2024.13690341369034Thermal transport of glasses via machine learning driven simulationsPaolo Pegolo0Federico Grasselli1SISSA—Scuola Internazionale Superiore di Studi Avanzati, Trieste, ItalyCOSMO—Laboratory of Computational Science and Modeling, Institut des Matériàux (IMX), École Polytechnique Fédérale de Lausanne, Lausanne, SwitzerlandAccessing 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.https://www.frontiersin.org/articles/10.3389/fmats.2024.1369034/fullthermal transportmachine learningglassesthermal propertiesGreen Kubo methodmolecular dynamics |
| spellingShingle | Paolo Pegolo Federico Grasselli Thermal transport of glasses via machine learning driven simulations Frontiers in Materials thermal transport machine learning glasses thermal properties Green Kubo method molecular dynamics |
| title | Thermal transport of glasses via machine learning driven simulations |
| title_full | Thermal transport of glasses via machine learning driven simulations |
| title_fullStr | Thermal transport of glasses via machine learning driven simulations |
| title_full_unstemmed | Thermal transport of glasses via machine learning driven simulations |
| title_short | Thermal transport of glasses via machine learning driven simulations |
| title_sort | thermal transport of glasses via machine learning driven simulations |
| topic | thermal transport machine learning glasses thermal properties Green Kubo method molecular dynamics |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2024.1369034/full |
| work_keys_str_mv | AT paolopegolo thermaltransportofglassesviamachinelearningdrivensimulations AT federicograsselli thermaltransportofglassesviamachinelearningdrivensimulations |