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Unsupervised machine learning techniques have been combined with scanning transmission electron microscopy (STEM) to enable comprehensive crystal structure analysis with nanometer spatial resolution. In this study, we investigated large-scale data obtained by four-dimensional (4D) STEM using dimensionality reduction techniques such as non-negative matrix factorization (NMF) and hierarchical clustering with various optimization methods. We developed software scripts incorporating knowledge of electron diffraction and STEM imaging for data preprocessing, NMF, and hierarchical clustering. Hierarchical clustering was performed using cross-correlation instead of conventional Euclidean distances, resulting in rotation-corrected diffractions and shift-corrected maps of major components. An experimental analysis was conducted on a high-pressure-annealed metallic glass, Zr-Cu-Al, revealing an amorphous matrix and crystalline precipitates with an average diameter of approximately 7 nm, which were challenging to detect using conventional STEM techniques. Combining 4D-STEM and optimized unsupervised machine learning enables comprehensive bimodal (i.e., spatial and reciprocal) analyses of material nanostructures.

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• Creative Commons BY Attribution 4.0 International
  

  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this
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キーワード: Scanning Transmission Electron Microscopy, Unsupervised Machine Learning, 4D-STEM, Non-negative Matrix Factorization, Hierachical Clustering, Metallic Glass

刊行年月日: 2024-02-05

出版者: Springer Science and Business Media LLC

掲載誌:

• Scientific Reports (ISSN: 20452322) vol. 14 2901

研究助成金:

• Japan Society for the Promotion of Science JP 22H05145
• Japan Society for the Promotion of Science 20H02624
• Japan Society for the Promotion of Science JP23H04874

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1038/s41598-024-53289-5

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更新時刻: 2024-02-15 16:30:11 +0900

MDRでの公開時刻: 2024-02-15 16:30:11 +0900