論文

査読有り
2020年12月

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

Communications Materials
  • Akihiko Hirata
  • ,
  • Tomohide Wada
  • ,
  • Ippei Obayashi
  • ,
  • Yasuaki Hiraoka

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記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1038/s43246-020-00100-3
出版者・発行元
Springer Science and Business Media {LLC}

<title>Abstract</title>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.

リンク情報
DOI
https://doi.org/10.1038/s43246-020-00100-3
URL
http://www.nature.com/articles/s43246-020-00100-3.pdf
URL
http://www.nature.com/articles/s43246-020-00100-3
ID情報
  • DOI : 10.1038/s43246-020-00100-3
  • ISSN : 2662-4443
  • eISSN : 2662-4443
  • ORCIDのPut Code : 86538256
  • SCOPUS ID : 85104009929

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