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

Abstract

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 (Ni 62Nb 38, Al 90Sm 10 and Fe 80P 20). 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.

Authors:
 [1]; ORCiD logo [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); National Natural Science Foundation of China (NNSFC)
OSTI Identifier:
1582355
Grant/Contract Number:  
[AC02-05CH11231; 51701190]
Resource Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
[ Journal Volume: 10; Journal Issue: 1]; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Condensed-matter physics; Glasses; Metals and alloys; Structure of solids and liquids; Theory and computation

Citation Formats

Wang, Qi, and Jain, Anubhav. A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses. United States: N. p., 2019. Web. doi:10.1038/s41467-019-13511-9.
Wang, Qi, & Jain, Anubhav. A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses. United States. doi:10.1038/s41467-019-13511-9.
Wang, Qi, and Jain, Anubhav. Thu . "A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses". United States. doi:10.1038/s41467-019-13511-9. https://www.osti.gov/servlets/purl/1582355.
@article{osti_1582355,
title = {A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses},
author = {Wang, Qi and Jain, Anubhav},
abstractNote = {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.},
doi = {10.1038/s41467-019-13511-9},
journal = {Nature Communications},
number = [1],
volume = [10],
place = {United States},
year = {2019},
month = {12}
}

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