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Chin. Phys. B, 2021, Vol. 30(5): 057103    DOI: 10.1088/1674-1056/abdda5
Special Issue: SPECIAL TOPIC — Machine learning in condensed matter physics
SPECIAL TOPIC—Machine learning in condensed matter physics Prev   Next  

Quantitative structure-plasticity relationship in metallic glass: A machine learning study

Yicheng Wu(吴义成)1, Bin Xu(徐斌)1, Yitao Sun(孙奕韬)2, and Pengfei Guan(管鹏飞)1,†
1 Beijing Computational Science Research Center, Beijing 100193, China;
2 Institutes of Physics, Chinese Academy of Sciences, Beijing 100190, China
Abstract  The lack of the long-range order in the atomic structure challenges the identification of the structural defects, akin to dislocations in crystals, which are responsible for predicting plastic events and mechanical failure in metallic glasses (MGs). Although vast structural indicators have been proposed to identify the structural defects, quantitatively gauging the correlations between these proposed indicators based on the undeformed configuration and the plasticity of MGs upon external loads is still lacking. Here, we systematically analyze the ability of these indicators to predict plastic events in a representative MG model using machine learning method. Moreover, we evaluate the influences of coarse graining method and medium-range order on the predictive power. We demonstrate that indicators relevant to the low-frequency vibrational modes reveal the intrinsic structural characteristics of plastic rearrangements. Our work makes an important step towards quantitative assessments of given indicators, and thereby an effective identification of the structural defects in MGs.
Keywords:  metallic glass      structure      plasticity      machine learning  
Received:  09 December 2020      Revised:  14 January 2021      Accepted manuscript online:  20 January 2021
PACS:  71.23.Cq (Amorphous semiconductors, metallic glasses, glasses)  
  61.25.Mv (Liquid metals and alloys)  
  81.40.Lm (Deformation, plasticity, and creep)  
Fund: Project supported by the Science Challenge Project (Grant No. TZ2018004), the NSAF Joint Program (Grant No. U1930402), the National Natural Science Foundation of China (Grant No. 51801230), and the National Key Research and Development Program of China (Grant No. 2018YFA0703601).
Corresponding Authors:  Pengfei Guan     E-mail:  pguan@csrc.ac.cn

Cite this article: 

Yicheng Wu(吴义成), Bin Xu(徐斌), Yitao Sun(孙奕韬), and Pengfei Guan(管鹏飞) Quantitative structure-plasticity relationship in metallic glass: A machine learning study 2021 Chin. Phys. B 30 057103

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