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

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