Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon
文献类型：期刊论文
| 作者 | Qian X.; 彭神佑.; Li X.; Wei YJ(魏宇杰) ; Yang R.
|
| 刊名 | MATERIALS TODAY PHYSICS
 |
| 出版日期 | 2019-08 |
| 卷号 | 10页码:UNSP 100140 |
| 关键词 | Thermal conductivity Machine learning Molecular dynamics Phonons |
| ISSN号 | 2542-5293 |
| DOI | 10.1016/j.mtphys.2019.100140 |
| 英文摘要 | First 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. |
| WOS关键词 | APPROXIMATION |
| WOS研究方向 | Materials Science, Multidisciplinary ; Physics, Applied |
| 语种 | 英语 |
| WOS记录号 | WOS:000511431800008 |
| 资助机构 | NSFNational Science Foundation (NSF) [ACI-1532235, ACI-1532236, 1512776] ; University of Colorado Boulder ; Colorado State University ; Supercomputing Center of Chinese Academy of Sciences |
| 其他责任者 | Yang, R |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/81442]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。