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We present a comprehensive approach to characterizing labyrinthine structuresthat often emerge as a final steady state in pattern forming systems. We employmachine learning based pattern recognition techniques to identify the types andlocations of topological defects of the local stripe ordering to augmentconventional Fourier analysis. A pair distribution function analysis of thetopological defects reveals subtle differences between labyrinthine structureswhich are beyond the conventional characterization methods. We utilize ourapproach to highlight a clear morphological transition between two zero-fieldlabyrinthine structures in single crystal Bi substituted Yttrium Iron Garnetfilms. An energy landscape picture is proposed to understand the athermaldynamics that governs the observed morphological transition. Our workdemonstrates that machine learning based recognition techniques enable novelstudies of rich and complex labyrinthine type structures universal to manypattern formation systems.

Machine Learning Assisted Characterization of Labyrinthine Pattern Transitions