Using characteristic structural motifs in metallic liquids to predict glass forming ability

WP Weeks and KM Flores, INTERMETALLICS, 145, 107560 (2022).

DOI: 10.1016/j.intermet.2022.107560

Despite intense interest in the discovery and design of metallic glasses, the efficient a priori identification of novel glass-formers without the need for time-consuming experimental characterization has remained an unattained goal. To address this, we use geometric alignment and density-based clustering algorithms to quantitatively describe the short-range atomic structure in the simulated liquid state for five known metallic glass-forming systems. We show that each liquid is comprised of a surprisingly small number of geometrically-similar atomic clusters (6-8 characteristic motifs in the systems studied) and that the variance of the population distribution of these clusters in the high temperature liquid is inversely correlated to the experimentally- observed glass-forming ability (GFA) as a function of composition within each system studied. These correlations are observed without consideration of temperature-dependent evolution or longer range atomic arrangements, which are much more time-consuming to evaluate. The relative simplicity and broad applicability of this technique to both good glass-forming systems (Cu-Zr, Ni-Nb, Al-Ni-Zr) and poor glass- forming systems (Al-Sm, Au-Si) suggests that the population of characteristic atomic clusters in the simulated liquid could be used as an efficient, high-throughput screening method for identification of potential glass-forming alloys.

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