51. Structural changes during glass formation extracted by computational homology with machine learning
- Author
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Yasuaki Hiraoka, Ippei Obayashi, Tomohide Wada, and Akihiko Hirata
- Subjects
Materials science ,Amorphous metal ,Persistent homology ,010304 chemical physics ,business.industry ,0102 computer and information sciences ,Cooling rates ,Static structure ,Computational homology ,Machine learning ,computer.software_genre ,Condensed Matter::Disordered Systems and Neural Networks ,01 natural sciences ,Cooling rate ,Order (biology) ,010201 computation theory & mathematics ,Mechanics of Materials ,0103 physical sciences ,General Materials Science ,Artificial intelligence ,Linear machine ,business ,computer - 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.
- Published
- 2020
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