Structural changes during glass formation extracted by computational homology with machine learning

Abstract

The structural origin of the slow dynamics in glass formation remains to be understood owing to the subtle structural differences between the liquid and glass states. Even from simulations, where the positions of all atoms are deterministic, it is difficult to extract significant structural components for glass formation. In this study, we have extracted significant local atomic structures from a large number of metallic glass models with different cooling rates by utilising a computational persistent homology method combined with linear machine learning techniques. A drastic change in the extended range atomic structure consisting of 3–9 prism-type atomic clusters, rather than a change in individual atomic clusters, was found during the glass formation. The present method would be helpful towards understanding the hierarchical features of the unique static structure of the glass states. In glass formation, the dynamics of extended structures beyond atomic short-range order is yet to be understood. Here, persistent homology, combined with machine learning, reveals superstructures made of 3-to-9 prism-type atomic clusters which undergo drastic changes according to the glass cooling rate.