Article Linking structure and process in dendritic growth using persistent homology with energy analysis

Misato Tone (Department of Material Science and Technology, Tokyo University of Science) ; Shunsuke Sato (Department of Material Science and Technology, Tokyo University of Science) ; Sotaro Kunii (Department of Material Science and Technology, Tokyo University of Science) ; Ippei Obayashi (Center for Artificial Intelligence and Mathematical Data Science, Okayama University) ; Yasuaki Hiraoka (Kyoto University Institute for Advanced Study, Kyoto University) ; Yui Ogawa (NTT Basic Research Laboratories, Atsugi) ; Hirokazu Fukidome (Research Institute of Electrical Communication, Tohoku University) ; Alexandre Lira Foggiatto (Department of Material Science and Technology, Tokyo University of Science) ; Chiharu Mitsumata (Department of Material Science and Technology, Graduate school of Pure and Applied Science, University of Tsukuba, Tokyo University of Science) ; Ryunsuke Nagaoka (Department of Material Science and Technology, Tokyo University of Science) ; Arpita Varadwaj (Department of Material Science and Technology, Tokyo University of Science) ; Iwao Matsuda (Institute for Solid State Physics, The University of Tokyo, Kashiwa) ; Masato Kotsugi (Department of Material Science and Technology, Tokyo University of Science)

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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

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

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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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