Journal article Linking structure and process in dendritic growth using persistent homology with energy analysis
Misato Tone (author) (Search by this author)
Department of Material Science and Technology, Tokyo University of Science
;
Shunsuke Sato (author) (Search by this author)
Department of Material Science and Technology, Tokyo University of Science
;
Sotaro Kunii (author) (Search by this author)
Department of Material Science and Technology, Tokyo University of Science
;
Ippei Obayashi (author) (Search by this author)
Center for Artificial Intelligence and Mathematical Data Science, Okayama University
;
Yasuaki Hiraoka (author) (Search by this author)
Kyoto University Institute for Advanced Study, Kyoto University
;
Yui Ogawa (author) (Search by this author)
NTT Basic Research Laboratories, Atsugi
;
Hirokazu Fukidome (author) (Search by this author)
Research Institute of Electrical Communication, Tohoku University
;
Alexandre Lira Foggiatto (author) (Search by this author)
Department of Material Science and Technology, Tokyo University of Science
;
Chiharu Mitsumata (author) (Search by this author)
Department of Material Science and Technology, Graduate school of Pure and Applied Science, University of Tsukuba, Tokyo University of Science
;
Ryunsuke Nagaoka (author) (Search by this author)
Department of Material Science and Technology, Tokyo University of Science
;
Arpita Varadwaj (author) (Search by this author)
Department of Material Science and Technology, Tokyo University of Science
;
Iwao Matsuda (author) (Search by this author)
Institute for Solid State Physics, The University of Tokyo, Kashiwa
;
Masato Kotsugi (author) (Search by this author)
Department of Material Science and Technology, Tokyo University of Science
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Citation
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

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(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

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Keyword: structure-process linkage, persistent homology, dendrite growth, interpretable machine learning, free energy

Date published: 2025-12-31

Publisher: Taylor & Francis

Journal:

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

Funding:

Manuscript type: Author's version (Accepted manuscript)

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

First published URL: https://doi.org/10.1080/27660400.2025.2475735

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Updated at: 2025-07-18 10:20:37 +0900

Published on MDR: 2025-03-12 12:30:13 +0900

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