Machine learning bridges local static structure with multiple properties in metallic glasses
Abstract
A long-standing challenge in the metallic glass (MG) community has been how to quantitatively gauge the influence of the intricate local packing environment on the response (such as the propensity for atomic rearrangement) of the atomic configuration to external stimuli. Here we establish this structure–property relation by representing the complex amorphous structure using a single, flexibility-orientated structural quantity. This structural flexibility (SF) couples to a bona fide structural representation, the pair distribution function (PDF) of individual atoms, through a weighting function that reflects what matters in the static atomic configuration to dynamic responses. Machine learning is used, employing microscopic flexibility volume as the supervisory signal, to establish via direct regression an optimized weighting vector, which is proven robust for all quenching rates, deformation conditions, and different compositions in a given (e.g., CuxZr100-x) alloy system. Additionally, the SF is evaluated solely from the particle positions (PDF), for any structure variation, from the atomic scale up to sample average. Strong correlations are demonstrated between SF and a broad range of properties, including vibrational, diffusional, as well as elastic and plastic relaxation responses.
- Authors:
-
- Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Materials Science and Engineering
- Xi'an Jiaotong Univ., Shaanxi (China). State Key Lab. for Mechanical Behavior of Materials. Center for Advancing Materials Performance from the Nanoscale (CAMP-Nano)
- Publication Date:
- Research Org.:
- Johns Hopkins Univ., Baltimore, MD (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1849479
- Alternate Identifier(s):
- OSTI ID: 1728469
- Grant/Contract Number:
- FG02-03ER46056; AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Materials Today
- Additional Journal Information:
- Journal Volume: 40; Journal Issue: C; Journal ID: ISSN 1369-7021
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING; materials science; metallic glasses; structure-property relations; simulation; machine learning
Citation Formats
Fan, Zhao, Ding, Jun, and Ma, Evan. Machine learning bridges local static structure with multiple properties in metallic glasses. United States: N. p., 2020.
Web. doi:10.1016/j.mattod.2020.05.021.
Fan, Zhao, Ding, Jun, & Ma, Evan. Machine learning bridges local static structure with multiple properties in metallic glasses. United States. https://doi.org/10.1016/j.mattod.2020.05.021
Fan, Zhao, Ding, Jun, and Ma, Evan. Sat .
"Machine learning bridges local static structure with multiple properties in metallic glasses". United States. https://doi.org/10.1016/j.mattod.2020.05.021. https://www.osti.gov/servlets/purl/1849479.
@article{osti_1849479,
title = {Machine learning bridges local static structure with multiple properties in metallic glasses},
author = {Fan, Zhao and Ding, Jun and Ma, Evan},
abstractNote = {A long-standing challenge in the metallic glass (MG) community has been how to quantitatively gauge the influence of the intricate local packing environment on the response (such as the propensity for atomic rearrangement) of the atomic configuration to external stimuli. Here we establish this structure–property relation by representing the complex amorphous structure using a single, flexibility-orientated structural quantity. This structural flexibility (SF) couples to a bona fide structural representation, the pair distribution function (PDF) of individual atoms, through a weighting function that reflects what matters in the static atomic configuration to dynamic responses. Machine learning is used, employing microscopic flexibility volume as the supervisory signal, to establish via direct regression an optimized weighting vector, which is proven robust for all quenching rates, deformation conditions, and different compositions in a given (e.g., CuxZr100-x) alloy system. Additionally, the SF is evaluated solely from the particle positions (PDF), for any structure variation, from the atomic scale up to sample average. Strong correlations are demonstrated between SF and a broad range of properties, including vibrational, diffusional, as well as elastic and plastic relaxation responses.},
doi = {10.1016/j.mattod.2020.05.021},
journal = {Materials Today},
number = C,
volume = 40,
place = {United States},
year = {Sat Jun 20 00:00:00 EDT 2020},
month = {Sat Jun 20 00:00:00 EDT 2020}
}
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