Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space

doi:10.1103/PhysRevB.104.064108

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	Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space
	Yang ZY(杨增宇)4,5; Wei D(魏丹)4,5; Zaccone, Alessio 1,2,3; Wang YJ(王云江)4,5
刊名	PHYSICAL REVIEW B
	2021-08-13
卷号	104 期号:6 页码:14
ISSN号	2469-9950
DOI	10.1103/PhysRevB.104.064108
通讯作者	Wang, Yun-Jiang(yjwang@imech.ac.cn)
英文摘要	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.
分类号	二类
资助项目	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]
WOS关键词	MECHANICAL-BEHAVIOR ; INHOMOGENEOUS FLOW ; RELAXATION ; LIQUIDS ; DEFORMATION ; ORDER
WOS研究方向	Materials Science ; Physics
语种	英语
WOS记录号	WOS:000685105800002
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
其他责任者	Wang, Yun-Jiang
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/87221]
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 Cambridge, Cavendish Lab, Cambridge CB3 0HE, England 2.Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge CB3 0AS, England; 3.Univ Milan, Dept Phys A Pontremoli, Via Celoria 16, I-20133 Milan, Italy; 4.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China; 5.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China;
推荐引用方式 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.
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.