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In situ scanning transmission electron microscopy enables observation of thedomain dynamics in ferroelectric materials as a function of externally appliedbias and temperature. The resultant data sets contain a wealth of informationon polarization switching and phase transition mechanisms. However,identification of these mechanisms from observational data sets has remained aproblem due to a large variety of possible configurations, many of which aredegenerate. Here, we introduce an approach based on rotationally invariantvariational autoencoder (VAE), which enables learning a latent spacerepresentation of the data with multiple real-space rotationally equivalentvariants mapped to the same latent space descriptors. By varying the size oftraining sub-images in the VAE, we tune the degree of complexity in thestructural descriptors from simple domain wall detection to the identificationof switching pathways. This yields a powerful tool for the exploration of thedynamic data in mesoscopic electron, scanning probe, optical, and chemicalimaging. Moreover, this work adds to the growing body of knowledge ofincorporating physical constraints into the machine and deep-learning methodsto improve learned descriptors of physical phenomena.

Latent mechanisms of polarization switching from in situ electron microscopy observations