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.
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).
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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