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Machine-learning-assisted characterization of labyrinthine pattern transitions

Kotaro Shimizu1,2,*, Vinicius Yu Okubo3, Rose Knight1, Ziyuan Wang1, Joseph Burton1, Hae Yong Kim3, Gia-Wei Chern1, and B. S. Shivaram1

  • *Contact author: shimizu@ap.t.u-tokyo.ac.jp
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APS Open Sci. 1, 000105 – Published 24 August, 2026

DOI: https://doi.org/10.1103/7srq-cz52

Abstract

Labyrinthine structures are ubiquitous in pattern-forming systems arising from a wide variety of physical, chemical, and biological processes. Yet, their characterization remains challenging due to the lack of long-range order and the resultant complicated morphologies. Here, we present an approach for characterizing labyrinthine patterns based on the identification and the correlation analysis of their topological defects. We employ advanced machine-learning-based-pattern-recognition techniques to identify the types and locations of topological defects of the local stripe ordering. Applying this method to single-crystal Bi-substituted yttrium iron garnet films, we uncover a distinct morphological transition between two zero-field labyrinthine structures. Crucially, the pair distribution functions of the topological defects reveal subtle differences between labyrinthine structures, which are not discernible from conventional global analysis. By systematically analyzing the spatial correlations and geometric properties of these defects, we provide insights into the athermal dynamics that govern the observed morphological transitions. Our work demonstrates that machine-learning-based recognition techniques enable systematic studies of rich and complex labyrinthine-type structures universal to many pattern-formation systems.

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APS Open Sci. 1, 000105– Published 24 August, 2026
Vol. 1
  • Received 25 January 2026
  • Accepted 14 July 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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