2025-04-08 京都大学
＜関連情報＞
- https://www.kyoto-u.ac.jp/ja/research-news/2025-04-08-4
- https://www.kyoto-u.ac.jp/sites/default/files/2025-04/web_2504_Hiraoka-637ae7d943685c7562c15662a63e2a2f.pdf
- https://www.tandfonline.com/doi/full/10.1080/27660400.2025.2475735
エネルギー解析を用いた持続的相同性による樹状突起成長の構造とプロセスの関連付け Linking structure and process in dendritic growth using persistent homology with energy analysis
Misato Tone,Shunsuke Sato,Sotaro Kunii,Ippei Obayashi,Yasuaki Hiraoka,Yui Ogawai,…
Science and Technology of Advanced Materials: Methods Published:08 Apr 2025
DOI:https://doi.org/10.1080/27660400.2025.2475735
GRAPHICAL ABSTRACT
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.
