The study uses computational persistent homology and machine learning to analyze the local atomic structures of metallic glass models with varying cooling rates. It finds that a significant change in the extended-range atomic structure, consisting of 3–9 prism-type atomic clusters, occurs during glass formation, rather than changes in individual clusters. This method aids in understanding the hierarchical structure of glass states.
