Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures

One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as density functional theory (DFT) provide the best possible accuracy on electronic properties but the...

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Autores: Mortazavi, Bohayra|||0000-0003-3031-5057, Podryabinkin, Evgeny V., Roche, Stephan|||0000-0003-0323-4665, Rabczuk, Timon, Zhuang, Xiaoying|||0000-0001-6562-2618, Shapeev, Alexander V.
Formato: artículo
Fecha de publicación:2020
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:236026
Acesso em linha:https://ddd.uab.cat/record/236026
https://dx.doi.org/urn:doi:10.1039/d0mh00787k
Access Level:acceso abierto
Palavra-chave:Ab initio molecular dynamics
Classical molecular dynamics
Computational design
First-principles approaches
Interatomic potential
Lattice thermal conductivity
Macroscopic structure
Multi-scale Modeling
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spelling Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructuresMortazavi, Bohayra|||0000-0003-3031-5057Podryabinkin, Evgeny V.Roche, Stephan|||0000-0003-0323-4665Rabczuk, TimonZhuang, Xiaoying|||0000-0001-6562-2618Shapeev, Alexander V.Ab initio molecular dynamicsClassical molecular dynamicsComputational designFirst-principles approachesInteratomic potentialLattice thermal conductivityMacroscopic structureMulti-scale ModelingOne of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as density functional theory (DFT) provide the best possible accuracy on electronic properties but they are limited to systems up to a few hundreds, or at most thousands of atoms. On the other hand, classical molecular dynamics (CMD) simulations and the finite element method (FEM) are extensively employed to study larger and more realistic systems, but conversely depend on empirical information. Here, we show that machine-learning interatomic potentials (MLIPs) trained over short ab initio molecular dynamics trajectories enable first-principles multiscale modeling, in which DFT simulations can be hierarchically bridged to efficiently simulate macroscopic structures. As a case study, we analyze the lattice thermal conductivity of coplanar graphene/borophene heterostructures, recently synthesized experimentally (Sci. Adv., 2019, 5, eaax6444), for which no viable classical modeling alternative is presently available. Our MLIP-based approach can efficiently predict the lattice thermal conductivity of graphene and borophene pristine phases, the thermal conductance of complex graphene/borophene interfaces and subsequently enable the study of effective thermal transport along the heterostructures at continuum level. This work highlights that MLIPs can be effectively and conveniently employed to enable first-principles multiscale modeling via hierarchical employment of DFT/CMD/FEM simulations, thus expanding the capability for computational design of novel nanostructures. 22020-01-0120202020-01-01Articlehttp://purl.org/coar/resource_type/c_6501SMURhttp://purl.org/coar/version/c_71e4c1898caa6e32info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/236026https://dx.doi.org/urn:doi:10.1039/d0mh00787kreponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengMinisterio de Economía y Competitividad https://doi.org/10.13039/501100003329 SEV-2017-0706open accesshttp://purl.org/coar/access_right/c_abf2Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.https://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2360262026-06-06T12:50:31Z
dc.title.none.fl_str_mv Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
title Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
spellingShingle Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
Mortazavi, Bohayra|||0000-0003-3031-5057
Ab initio molecular dynamics
Classical molecular dynamics
Computational design
First-principles approaches
Interatomic potential
Lattice thermal conductivity
Macroscopic structure
Multi-scale Modeling
title_short Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
title_full Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
title_fullStr Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
title_full_unstemmed Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
title_sort Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
dc.creator.none.fl_str_mv Mortazavi, Bohayra|||0000-0003-3031-5057
Podryabinkin, Evgeny V.
Roche, Stephan|||0000-0003-0323-4665
Rabczuk, Timon
Zhuang, Xiaoying|||0000-0001-6562-2618
Shapeev, Alexander V.
author Mortazavi, Bohayra|||0000-0003-3031-5057
author_facet Mortazavi, Bohayra|||0000-0003-3031-5057
Podryabinkin, Evgeny V.
Roche, Stephan|||0000-0003-0323-4665
Rabczuk, Timon
Zhuang, Xiaoying|||0000-0001-6562-2618
Shapeev, Alexander V.
author_role author
author2 Podryabinkin, Evgeny V.
Roche, Stephan|||0000-0003-0323-4665
Rabczuk, Timon
Zhuang, Xiaoying|||0000-0001-6562-2618
Shapeev, Alexander V.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ab initio molecular dynamics
Classical molecular dynamics
Computational design
First-principles approaches
Interatomic potential
Lattice thermal conductivity
Macroscopic structure
Multi-scale Modeling
topic Ab initio molecular dynamics
Classical molecular dynamics
Computational design
First-principles approaches
Interatomic potential
Lattice thermal conductivity
Macroscopic structure
Multi-scale Modeling
description One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as density functional theory (DFT) provide the best possible accuracy on electronic properties but they are limited to systems up to a few hundreds, or at most thousands of atoms. On the other hand, classical molecular dynamics (CMD) simulations and the finite element method (FEM) are extensively employed to study larger and more realistic systems, but conversely depend on empirical information. Here, we show that machine-learning interatomic potentials (MLIPs) trained over short ab initio molecular dynamics trajectories enable first-principles multiscale modeling, in which DFT simulations can be hierarchically bridged to efficiently simulate macroscopic structures. As a case study, we analyze the lattice thermal conductivity of coplanar graphene/borophene heterostructures, recently synthesized experimentally (Sci. Adv., 2019, 5, eaax6444), for which no viable classical modeling alternative is presently available. Our MLIP-based approach can efficiently predict the lattice thermal conductivity of graphene and borophene pristine phases, the thermal conductance of complex graphene/borophene interfaces and subsequently enable the study of effective thermal transport along the heterostructures at continuum level. This work highlights that MLIPs can be effectively and conveniently employed to enable first-principles multiscale modeling via hierarchical employment of DFT/CMD/FEM simulations, thus expanding the capability for computational design of novel nanostructures.
publishDate 2020
dc.date.none.fl_str_mv 2
2020-01-01
2020
2020-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
SMUR
http://purl.org/coar/version/c_71e4c1898caa6e32
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/236026
https://dx.doi.org/urn:doi:10.1039/d0mh00787k
url https://ddd.uab.cat/record/236026
https://dx.doi.org/urn:doi:10.1039/d0mh00787k
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad https://doi.org/10.13039/501100003329 SEV-2017-0706
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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