Machine learning atomic-scale stiffness in metallic glass

doi:10.1016/j.eml.2021.101446

IMECH-IR > 非线性力学国家重点实验室

	Machine learning atomic-scale stiffness in metallic glass
	Peng ZH(彭正瀚)1,2; Yang ZY(杨增宇)1,3 ; Wang YJ(王云江)1,3
通讯作者	Wang, Yun-Jiang(yjwang@imech.ac.cn)
发表期刊	EXTREME MECHANICS LETTERS
	2021-10-01
卷号	48 页码:5
ISSN	2352-4316
摘要	Due to lack of either translational or rotational symmetries at atomic-scale, predicting properties of amorphous materials from static structure is a challenging task. To circumvent the dilemma, a supervised machine-learning strategy via neural network is proposed to predict the atomic stiffness of metallic glass from discretized radial distribution function. The predicted stiffness and its spatial nature are calibrated with molecular dynamics simulations. After which, the origin of atomic constraint is interpreted via the learning structural input. Inadequacy of the model is discussed in terms of incompleteness in both machine-learning configurational space and structural descriptor. (C) 2021 Elsevier Ltd. All rights reserved.
关键词	Metallic glass Machine learning Atomic stiffness Molecular dynamics
DOI	10.1016/j.eml.2021.101446
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000686901700002
关键词[WOS]	MECHANICAL-BEHAVIOR ; DYNAMICS ; DEFORMATION ; RELAXATION ; SIMULATION ; DEFECTS ; ENTROPY ; FLOW
WOS研究方向	Engineering ; Materials Science ; Mechanics
WOS类目	Engineering, Mechanical ; Materials Science, Multidisciplinary ; Mechanics
资助项目	National Key Research and Development Program of China[2017YFB0701502] ; National Key Research and Development Program of China[2017YFB0702003] ; National Natural Science Foundation of China[12072344] ; National Natural Science Foundation of China[11790292] ; Youth Innovation Promotion Association of Chinese Academy of Sciences, China[2017025]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association of Chinese Academy of Sciences, China
论文分区	一类
力学所作者排名	1
RpAuthor	Wang, Yun-Jiang
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/87261
专题	非线性力学国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; 2.Sichuan Univ, Coll Mat Sci & Engn, Chengdu 610065, Peoples R China; 3.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Peng ZH,Yang ZY,Wang YJ. Machine learning atomic-scale stiffness in metallic glass[J]. EXTREME MECHANICS LETTERS,2021,48:5.
APA	彭正瀚,杨增宇,&王云江.(2021).Machine learning atomic-scale stiffness in metallic glass.EXTREME MECHANICS LETTERS,48,5.
MLA	彭正瀚,et al."Machine learning atomic-scale stiffness in metallic glass".EXTREME MECHANICS LETTERS 48(2021):5.

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