| 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
| |
| Corresponding Author | Wang, Yun-Jiang([email protected]) |
| Source Publication | EXTREME MECHANICS LETTERS
 |
| 2021-10-01 | |
| Volume | 48Pages:5 |
| ISSN | 2352-4316 |
| Abstract | 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. |
| Keyword | Metallic glass Machine learning Atomic stiffness Molecular dynamics |
| DOI | 10.1016/j.eml.2021.101446 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000686901700002 |
| WOS Keyword | MECHANICAL-BEHAVIOR ; DYNAMICS ; DEFORMATION ; RELAXATION ; SIMULATION ; DEFECTS ; ENTROPY ; FLOW |
| WOS Research Area | Engineering ; Materials Science ; Mechanics |
| WOS Subject | Engineering, Mechanical ; Materials Science, Multidisciplinary ; Mechanics |
| Funding Project | 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] |
| Funding Organization | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association of Chinese Academy of Sciences, China |
| Classification | 一类 |
| Ranking | 1 |
| Contributor | Wang, Yun-Jiang |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/87261 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 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 |
| 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.Rp_Au:Wang, Yun-Jiang |
| 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. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Jp2021F357.pdf（1663KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment