Latent mechanisms of polarization switching from in situ electron microscopy observations

TL;DR

This paper introduces a rotationally invariant variational autoencoder to analyze in situ electron microscopy data, enabling the identification of polarization switching mechanisms in ferroelectric materials.

Contribution

It presents a novel machine learning approach that captures rotationally invariant features and explores complex polarization switching pathways from microscopy data.

Findings

The method effectively distinguishes different polarization states.

It adapts to various structural complexities by tuning training parameters.

The approach enhances understanding of domain dynamics in ferroelectrics.

Abstract

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data sets has remained a problem due to a large variety of possible configurations, many of which are degenerate. Here, we introduce an approach based on rotationally invariant variational autoencoder (VAE), which enables learning a latent space representation of the data with multiple real-space rotationally equivalent variants mapped to the same latent space descriptors. By varying the size of training sub-images in the VAE, we tune the degree of complexity in the structural descriptors from simple domain wall detection to the identification of switching pathways. This yields a powerful tool for the exploration of the dynamic data in mesoscopic electron, scanning probe, optical, and chemical imaging. Moreover, this work adds to the growing body of knowledge of incorporating physical constraints into the machine and deep-learning methods to improve learned descriptors of physical phenomena.