4.2 Article

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

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COMMUNICATIONS MATERIALS
卷 1, 期 1, 页码 -

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SPRINGERNATURE
DOI: 10.1038/s43246-020-00100-3

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资金

  1. Fusion Research Funds from the World Premier International (WPI) Research Center Initiative for Atoms, Molecules and Materials programme of MEXT of Japan
  2. Japan Society for the Promotion of Science (JSPS) [16K17638, 20H04241, 17H01325, 19H00834, 20H00119]
  3. JST CREST Mathematics [15656429]
  4. JST MIRAI Programme [JPMJMI18G3]
  5. JST PRESTO [JPMJPR1923]
  6. Cross-ministerial Strategic Innovation Promotion Programme (SIP, Structural Materials for Innovation D72) of the Ministry of Agriculture
  7. New Energy and Industrial Technology Development Organization (NEDO)
  8. Council for Science, Technology and Innovation (CSTI)
  9. Cross-ministerial Strategic Innovation Promotion Programme (SIP)
  10. Materials Integration for revolutionary design system of structural materials (Funding agency: JST)
  11. Grants-in-Aid for Scientific Research [17H01325, 19H00834, 20H04241, 20H00119, 16K17638] Funding Source: KAKEN

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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.

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