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Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning

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  • Zhao Fan

    (Johns Hopkins University)

  • Evan Ma

    (Johns Hopkins University)

Abstract

It has been a long-standing materials science challenge to establish structure-property relations in amorphous solids. Here we introduce a rotationally non-invariant local structure representation that enables different predictions for different loading orientations, which is found essential for high-fidelity prediction of the propensity for stress-driven shear transformations. This novel structure representation, when combined with convolutional neural network (CNN), a powerful deep learning algorithm, leads to unprecedented accuracy for identifying atoms with high propensity for shear transformations (i.e., plastic susceptibility), solely from the static structure in both two- and three-dimensional model glasses. The data-driven models trained on samples at one composition and a given processing history are found transferrable to glass samples with different processing histories or at different compositions in the same alloy system. Our analysis of the new structure representation also provides valuable insight into key atomic packing features that influence the local mechanical response and its anisotropy in glasses.

Suggested Citation

  • Zhao Fan & Evan Ma, 2021. "Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21806-z
    DOI: 10.1038/s41467-021-21806-z
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    Cited by:

    1. Zhao Fan & Hajime Tanaka, 2024. "Microscopic mechanisms of pressure-induced amorphous-amorphous transitions and crystallisation in silicon," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Francesc Font-Clos & Marco Zanchi & Stefan Hiemer & Silvia Bonfanti & Roberto Guerra & Michael Zaiser & Stefano Zapperi, 2022. "Predicting the failure of two-dimensional silica glasses," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Zhen Wei Wu & Yixiao Chen & Wei-Hua Wang & Walter Kob & Limei Xu, 2023. "Topology of vibrational modes predicts plastic events in glasses," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

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