ジャーナル論文 Linking structure and process in dendritic growth using persistent homology with energy analysis
Misato Tone (author) (この著者で検索)
Tokyo University of Science Department of Material Science and Technology
;
Shunsuke Sato (author) (この著者で検索)
Tokyo University of Science Department of Material Science and Technology
;
Sotaro Kunii (author) (この著者で検索)
Tokyo University of Science Department of Material Science and Technology
;
Ippei Obayashi (author) (この著者で検索)
Okayama University Center for Artificial Intelligence and Mathematical Data Science
;
Yasuaki Hiraoka (author) (この著者で検索)
Kyoto University Kyoto University Institute for Advanced Study
;
Yui Ogawa (author) (この著者で検索)
NTT Basic Research Laboratories, Atsugi
;
Hirokazu Fukidome (author) (この著者で検索)
Tohoku University Research Institute of Electrical Communication
;
Alexandre Lira Foggiatto (author) (この著者で検索)
Tokyo University of Science Department of Material Science and Technology
;
Chiharu Mitsumata (author) (この著者で検索)
Tokyo University of Science Department of Material Science and Technology, Graduate school of Pure and Applied Science, University of Tsukuba
;
Ryunsuke Nagaoka (author) (この著者で検索)
Tokyo University of Science Department of Material Science and Technology
;
Arpita Varadwaj (author) (この著者で検索)
Tokyo University of Science Department of Material Science and Technology
;
Iwao Matsuda (author) (この著者で検索)
The University of Tokyo, Kashiwa Institute for Solid State Physics
;
Masato Kotsugi (author) (この著者で検索)
Tokyo University of Science Department of Material Science and Technology
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引用
Misato Tone, Shunsuke Sato, Sotaro Kunii, Ippei Obayashi, Yasuaki Hiraoka, Yui Ogawa, Hirokazu Fukidome, Alexandre Lira Foggiatto, Chiharu Mitsumata, Ryunsuke Nagaoka, Arpita Varadwaj, Iwao Matsuda, Masato Kotsugi. Linking structure and process in dendritic growth using persistent homology with energy analysis. Science and Technology of Advanced Materials: Methods. 2025, 25 (), 2475735. https://doi.org/10.1080/27660400.2025.2475735

説明:

(abstract)

We present a material analysis method that links structure and process in dendritic growth using explainable machine learning approaches. We employed persistent homology (PH) to quantitatively characterize the morphology of dendritic microstructures. By using interpretable machine learning with energy analysis, we established a robust relationship between structural features and Gibbs free energy. Through a detailed analysis of how Gibbs free energy evolves with morphological changes in dendrites, we uncovered specific conditions that influence the branching of dendritic structures. Moreover, energy gradient analysis based on morphological feature provides a deeper understanding of the branching mechanisms and offers a pathway to optimize thin-film growth processes. Integrating topology and free energy enables the optimization of a range of materials from fundamental research to practical

権利情報:

キーワード: structure-process linkage, persistent homology, dendrite growth, interpretable machine learning, free energy

刊行年月日: 2025-12-31

出版者: Taylor & Francis

掲載誌:

  • Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 25 2475735

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5360

公開URL: https://doi.org/10.1080/27660400.2025.2475735

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更新時刻: 2025-07-18 10:20:37 +0900

MDRでの公開時刻: 2025-03-12 12:30:13 +0900

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ファイル名 Linking structure and process in dendritic growth using persistent homology with energy analysis.pdf (サムネイル)
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