Article Uncovering crystal structure evolution via nanobeam X-ray diffraction with a continuity-driven machine learning approach

Zhendong Wu ORCID ; Tetsuya Tohei ORCID ; Yusuke Hayashi SAMURAI ORCID ; Shigeyoshi Usami ; Masayuki Imanishi ; Yusuke Mori ; Junichi Takino ; Kazushi Sumitani ; Yasuhiko Imai ORCID ; Shigeru Kimura ORCID ; Akira Sakai ORCID

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Zhendong Wu, Tetsuya Tohei, Yusuke Hayashi, Shigeyoshi Usami, Masayuki Imanishi, Yusuke Mori, Junichi Takino, Kazushi Sumitani, Yasuhiko Imai, Shigeru Kimura, Akira Sakai. Uncovering crystal structure evolution via nanobeam X-ray diffraction with a continuity-driven machine learning approach. Materials & Design. 2026, 263 (), 115669. https://doi.org/10.1016/j.matdes.2026.115669

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(abstract)

Nanobeam X-ray diffraction (nanoXRD) enables nanoscale mapping of crystal structures across wafer-scale crystals, offering unique insight into microstructural evolution during crystal growth. However, the resulting large and complex diffraction datasets make it challenging to quantitatively resolve local structural transitions and their connection to growth processes using conventional analysis. Here, we present a continuity-driven, unsupervised, and generalized analysis framework, referred to as the neighborhood-based similarity metric, which integrates spatial coordinates with nanoXRD data to reveal structural variations across growth sectors, interfaces, and defect-related regions without requiring prior knowledge or labels. By introducing Jaccard similarity scores to compare local neighborhoods in spatial and diffraction domains, the method quantitatively detects discontinuities where structural evolution disrupts the local continuity of diffraction patterns. Our unsupervised approach, validated with synthetic data and nanoXRD measurements of bulk GaN crystals, successfully identified both known defects and previously hidden structural discontinuities. The results provide new insights into the relationship between growth conditions, local strain evolution, and defect formation, establishing a robust and interpretable approach for linking processing and structural characteristics in complex crystalline materials.

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Keyword: GaN, nanoXRD, ML

Date published: 2026-02-15

Publisher: Elsevier BV

Journal:

  • Materials & Design (ISSN: 02641275) vol. 263 115669

Funding:

  • Murata Science and Education Foundation
  • Japan Society for the Promotion of Science JP16H06423
  • Japan Society for the Promotion of Science JP20H00352
  • Japan Society for the Promotion of Science JP22KK0055
  • Japan Society for the Promotion of Science JP23H01447

Manuscript type: Publisher's version (Version of record)

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First published URL: https://doi.org/10.1016/j.matdes.2026.115669

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Updated at: 2026-02-26 12:30:06 +0900

Published on MDR: 2026-02-26 09:41:08 +0900

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