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Persistent homology is a powerful tool for quantifying various structures, but it is equally crucial to maintain its interpretability for material design. In this study, we extracted interpretable geometric features from the persistent diagrams (PDs) of scanning transmission electron microscopy (STEM) images of self-assembled Pt-CeO2 nanostructures synthesized under different annealing conditions. Analysis of the PD quadrants provided five interpretable features: average width and total length of striped CeO2 phases, the number of CeO2 phases from zeroth PDs, and the numbers of ring- and arc-like structures from first PDs. Principal component analysis (PCA) and its component mapping onto PDs clarified that the number of small arc-like structures is especially important for describing Pt-CeO2 nano- structural changes. This descriptor enabled us to quantify the degree of disorder, namely the density of bends, in nanostructures formed under different conditions. By using this descriptor along with the width of the CeO2 phase, we could classify 12 Pt-CeO2 nanostructures in an interpretable way.

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キーワード: persistent homology

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会議: NIMS Award Symposium 2024 (2024-11-06 - 2024-11-07)

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原稿種別: 論文以外のデータ

MDR DOI: https://doi.org/10.48505/nims.5036

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更新時刻: 2024-11-27 16:30:45 +0900

MDRでの公開時刻: 2024-11-27 16:30:47 +0900