A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses

Wang, Qi; Jain, Anubhav

doi:10.1038/s41467-019-13511-9

Title: A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses

Journal Article · 04 December 2019 · Nature Communications

DOI: https://doi.org/10.1038/s41467-019-13511-9 · OSTI ID:1582355

Wang, Qi ^[1];

^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-uniform. However, the extent and manner in which atomic environment signatures present in the undeformed structure determine this plastic heterogeneity remain elusive. Here, we demonstrate that novel site environment features that characterize interstice distributions around atoms combined with machine learning (ML) can reliably identify plastic sites in several Cu-Zr compositions. Using only quenched structural information as input, the ML-based plastic probability estimates ("quench-in softness" metric) can identify plastic sites that could activate at high strains, losing predictive power only upon the formation of shear bands. Moreover, we reveal that a quench-in softness model trained on a single composition and quench rate substantially improves upon previous models in generalizing to different compositions and completely different MG systems (Ni₆₂Nb₃₈, Al₉₀Sm₁₀ and Fe₈₀P₂₀). Our work presents a general, data-centric framework that could potentially be used to address the structural origin of any site-specific property in MGs.

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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); National Natural Science Foundation of China (NSFC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1582355

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 10; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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