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Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon
Qian X.; 彭神佑.; Li X.; Wei YJ(魏宇杰); Yang R.
Source PublicationMATERIALS TODAY PHYSICS
2019-08
Volume10Pages:UNSP 100140
ISSN2542-5293
AbstractFirst principles-based modeling on phonon dynamics and transport using density functional theory and the Boltzmann transport equation has proven powerful in predicting thermal conductivity of crystalline materials, but it remains unfeasible for modeling complex crystals and disordered solids due to the prohibitive computational cost to capture the disordered structure, especially when the quasiparticle 'phonon' model breaks down. Recently, machine learning regression algorithms show great promises for building high-accuracy potential fields for atomistic modeling with length scales and timescales far beyond those achievable by first principles calculations. In this work, using both crystalline and amorphous silicon as examples, we develop machine learning-based potential fields for predicting thermal conductivity. The machine learning-based interatomic potential is derived from density functional theory calculations by stochastically sampling the potential energy surface in the configurational space. The thermal conductivities of both amorphous and crystalline silicon are then calculated using equilibrium molecular dynamics, which agree well with experimental measurements. This work documents the procedure for training the machine learning-based potentials for modeling thermal conductivity and demonstrates that machine learning-based potential can be a promising tool for modeling thermal conductivity of both crystalline and amorphous materials with strong disorder. (C) 2019 Elsevier Ltd. All rights reserved.
KeywordThermal conductivity Machine learning Molecular dynamics Phonons
DOI10.1016/j.mtphys.2019.100140
Indexed BySCI
Language英语
WOS IDWOS:000511431800008
WOS KeywordAPPROXIMATION
WOS Research AreaMaterials Science, Multidisciplinary ; Physics, Applied
WOS SubjectMaterials Science ; Physics
Funding OrganizationNSFNational Science Foundation (NSF) [ACI-1532235, ACI-1532236, 1512776] ; University of Colorado Boulder ; Colorado State University ; Supercomputing Center of Chinese Academy of Sciences
Ranking2
ContributorYang, R
Citation statistics
Document Type期刊论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/81432
Collection非线性力学国家重点实验室
Affiliation1.(Qian, X.
2.Yang, R.) Univ Colorado, Dept Mech Engn, Boulder, CO 80309 USA
3.(Peng, S.
4.Wei, Y.) Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing 100190, Peoples R China
5.(Li, X.) Huazhong Univ Sci & Technol, Sch Energy & Power Engn, State Key Lab Coal Combust, Wuhan 430074, Peoples R China
Recommended Citation
GB/T 7714
Qian X.,彭神佑.,Li X.,et al. Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon[J]. MATERIALS TODAY PHYSICS,2019,10:UNSP 100140.
APA Qian X.,彭神佑.,Li X.,魏宇杰,&Yang R..(2019).Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon.MATERIALS TODAY PHYSICS,10,UNSP 100140.
MLA Qian X.,et al."Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon".MATERIALS TODAY PHYSICS 10(2019):UNSP 100140.
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