Machine Learning Assisted Characterization of Labyrinthine Pattern Transitions

Abstract

We present a comprehensive approach to characterizing labyrinthine structures that often emerge as a final steady state in pattern forming systems. 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 beyond conventional characterization methods. By systematically analyzing the spatial correlations and geometric properties of these defects, we provide new insights into the athermal dynamics governing the observed morphological transitions. Our work demonstrates that machine learning based recognition techniques enable novel studies of rich and complex labyrinthine type structures universal to many pattern formation systems.