Using characteristic structural motifs in metallic liquids to predict glass forming ability. (June 2022)

• Record Type: Journal Article
• Authors: Weeks, W. Porter; Flores, Katharine M.
• Is Part Of: Intermetallics. Volume 145(2022)
• Publication Date: 2022-06
• Subjects: Metallic glass -- Molecular dynamics simulations -- Amorphous materials -- Glass forming ability; Intermetallic compounds -- Metallography -- Periodicals; Metallic glasses -- Periodicals; Composés intermétalliques -- Métallographie -- Périodiques; 669.94
• DOI: 10.1016/j.intermet.2022.107560
• Languages: English
• ISSNs: 0966-9795

Abstract

Abstract: 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. Highlights: ML-based clustering algorithm used to quantify order in metallic liquids. Structure of metallic liquid effectively characterized by 6–8 structural "motifs". Analysis of liquid structure provides predictor of GFA in various metallic systems.