中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Machine learning atomic-scale stiffness in metallic glass

文献类型:期刊论文

AuthorPeng ZH(彭正瀚)2,3; Yang ZY(杨增宇)1,3; Wang YJ(王云江)1,3
SourceEXTREME MECHANICS LETTERS
Issued Date2021-10-01
Volume48Pages:5
ISSN2352-4316
KeywordMetallic glass Machine learning Atomic stiffness Molecular dynamics
DOI10.1016/j.eml.2021.101446
Corresponding AuthorWang, Yun-Jiang(yjwang@imech.ac.cn)
English AbstractDue 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.
Classification一类
WOS KeywordMECHANICAL-BEHAVIOR ; DYNAMICS ; DEFORMATION ; RELAXATION ; SIMULATION ; DEFECTS ; ENTROPY ; FLOW
Funding ProjectNational 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 Research AreaEngineering ; Materials Science ; Mechanics
Language英语
WOS IDWOS:000686901700002
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association of Chinese Academy of Sciences, China
Other responsibleWang, Yun-Jiang
源URL[http://dspace.imech.ac.cn/handle/311007/87260]  
Collection力学研究所_非线性力学国家重点实验室
Affiliation1.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;
Recommended Citation
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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