| Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space | |
Yang ZY(杨增宇)1,2 ; Wei D(魏丹)1,2; Zaccone, Alessio3,4,5; Wang YJ(王云江)1,2
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| Corresponding Author | Wang, Yun-Jiang([email protected]) |
| Source Publication | PHYSICAL REVIEW B
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| 2021-08-13 | |
| Volume | 104Issue:6Pages:14 |
| ISSN | 2469-9950 |
| Abstract | Optimizing materials' properties and functions by controlling defects in the crystalline phase has been a cornerstone of materials science and condensed matter physics. However, this paradigm has yet to be established in the broadly defined amorphous materials, which implies the identification of very subtle structural features in an otherwise uniformly disordered medium. Here we propose and define a new integrated glassy defect (IGD), based on machine learning strategy informed by atomistic physics, and also by an extremely wide configurational, thermodynamic, and dynamic variables space of the disordered state. The IGD simultaneously includes positional topology and vibrational features, as well as the local morphology of the potential energy landscape. This unprecedented combination gives rise to a much more comprehensive and more effective definition of the "glassy defect," much beyond the conventional, purely structural input. IGD can be used not only as an efficient predictor of athermal plasticity but is also transferable to detect both short-time vibrational anomalies (the boson peak), and long-time relaxation and diffusion dynamics in glasses. The integrated strategy is instrumental to build the long-sought structure-property relationship in complex media. |
| DOI | 10.1103/PhysRevB.104.064108 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000685105800002 |
| WOS Keyword | MECHANICAL-BEHAVIOR ; INHOMOGENEOUS FLOW ; RELAXATION ; LIQUIDS ; DEFORMATION ; ORDER |
| WOS Research Area | Materials Science ; Physics |
| WOS Subject | Materials Science, Multidisciplinary ; Physics, Applied ; Physics, Condensed Matter |
| Funding Project | National Key Research and Development Program of China[2017YFB0701502] ; National Natural Science Foundation of China[12072344] ; Youth Innovation Promotion Association of Chinese Academy of Sciences[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 |
| Classification | 二类 |
| Ranking | 1 |
| Contributor | Wang, Yun-Jiang |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/87222 |
| 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.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China; 3.Univ Milan, Dept Phys A Pontremoli, Via Celoria 16, I-20133 Milan, Italy; 4.Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England; 5.Univ Cambridge, Cavendish Lab, Cambridge CB3 0HE, England |
| Recommended Citation GB/T 7714 | Yang ZY,Wei D,Zaccone, Alessio,et al. Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space[J]. PHYSICAL REVIEW B,2021,104,6,:14.Rp_Au:Wang, Yun-Jiang |
| APA | 杨增宇,魏丹,Zaccone, Alessio,&王云江.(2021).Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space.PHYSICAL REVIEW B,104(6),14. |
| MLA | 杨增宇,et al."Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space".PHYSICAL REVIEW B 104.6(2021):14. |
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| File Name/Size | DocType | Version | Access | License | ||
| Jp2021F370.pdf（4041KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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