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

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 applications.

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Publication Details

Journal
Science and Technology of Advanced Materials Methods
Published
2025-03-07
DOI
https://doi.org/10.1080/27660400.2025.2475735
Citations
2
Primary Topic
Topological and Geometric Data Analysis
Type
article
Field-Weighted Citation Impact
3.01

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article

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

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2 citations
Science and Technology of Advanced Materials Methods
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3.01
article

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

Hirokazu Fukidome, Masahide Tone, Shunsuke A. Sato, Chiharu Mitsumata, Iwao Matsuda, Alexandre Lira Foggiatto, Ippei Obayashi, Yasuaki Hiraoka, Masato Kotsugi, Sotaro Kunii, Arpita Varadwaj, Yui Ogawa, Ryunsuke Nagaoka
article en
2 citations

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 applications.

Science and Technology of Advanced Materials MethodsVol. 5(1)
University of Tsukuba (JP), Tokyo University of Science (JP), Okayama University (JP), Tohoku University (JP), Kyoto University (JP), NTT Basic Research Laboratories (JP), The University of Tokyo (JP)
Japan Society for the Promotion of Science, Japan Science and Technology Agency, JST-Mirai Program
Affordable and clean energy
Openalex Percentile: Top 10%
Topological and Geometric Data Analysis
3.01
DOIOpen Access PDFOpenAlex
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