Machine learning atomic-scale stiffness in metallic glass

被引：24

作者：

Peng, Zheng-Han ^{[1
,2
]}

Yang, Zeng-Yu ^{[1
,3
]}

Wang, Yun-Jiang ^{[1
,3
]}

机构：

[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

来源：

EXTREME MECHANICS LETTERS | 2021年 / 48卷

基金：

中国国家自然科学基金;

关键词：

Metallic glass; Machine learning; Atomic stiffness; Molecular dynamics; MECHANICAL-BEHAVIOR; DYNAMICS; DEFORMATION; RELAXATION; SIMULATION; DEFECTS; ENTROPY; FLOW;

D O I：

10.1016/j.eml.2021.101446

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

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.

引用

页数：5

共 53 条

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