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Machine learning atomic-scale stiffness in metallic glass

文献类型：期刊论文

作者Peng ZH(彭正瀚)2,3; Yang ZY(杨增宇)1,3; Wang YJ(王云江)1,3
刊名EXTREME MECHANICS LETTERS
出版日期2021-10-01
卷号48页码:5
ISSN号2352-4316
关键词Metallic glass Machine learning Atomic stiffness Molecular dynamics
DOI10.1016/j.eml.2021.101446
通讯作者Wang, Yun-Jiang(yjwang@imech.ac.cn)
英文摘要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.
分类号一类
WOS关键词MECHANICAL-BEHAVIOR ; DYNAMICS ; DEFORMATION ; RELAXATION ; SIMULATION ; DEFECTS ; ENTROPY ; FLOW
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]
WOS研究方向Engineering ; Materials Science ; Mechanics
语种英语
WOS记录号WOS:000686901700002
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association of Chinese Academy of Sciences, China
其他责任者Wang, Yun-Jiang
源URL[http://dspace.imech.ac.cn/handle/311007/87260]  
专题力学研究所_非线性力学国家重点实验室
作者单位1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
2.Sichuan Univ, Coll Mat Sci & Engn, Chengdu 610065, Peoples R China;
3.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, 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.

入库方式： OAI收割

来源：力学研究所

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