Journal
MATERIALS TODAY
Volume 40, Issue -, Pages 48-62Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.mattod.2020.05.021
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Funding
- U.S. Department of Energy (DOE) DOE-BES-DMSE [DE-FG02-19ER46056]
- Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
- Maryland Advanced Research Computing Center (MARCC)
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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. Subsequently, 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.
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