An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and are shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in the manuscript is available at https://github.com/saimani5/ferroelectric_domains_rVAE.
@article{arxiv.2101.06892,
title = {Unsupervised learning of ferroic variants from atomically resolved STEM images},
author = {Mani Valleti and Sergei V. Kalinin and Christopher T. Nelson and Jonathan J. P. Peters and Wen Dong and Richard Beanland and Xiaohang Zhang and Ichiro Takeuchi and Maxim Ziatdinov},
journal= {arXiv preprint arXiv:2101.06892},
year = {2024}
}