Disentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning

TL;DR

This paper uses unsupervised machine learning, specifically variational autoencoders, to analyze ferroelectric domain wall geometries and switching pathways in dynamic PFM images, revealing underlying mechanisms and microstructure correlations.

Contribution

It introduces a VAE-based approach that disentangles domain structure features and provides a novel way to visualize polarization switching in ferroelectric materials.

Findings

Latent space simplifies domain structures with rotational invariance.

Switching mechanisms are visualized in 2D latent space.

Method reveals correlations between domain evolution and microstructure.

Abstract

Domain switching pathways in ferroelectric materials visualized by dynamic Piezoresponse Force Microscopy (PFM) are explored via variational autoencoder (VAE), which simplifies the elements of the observed domain structure, crucially allowing for rotational invariance, thereby reducing the variability of local polarization distributions to a small number of latent variables. For small sampling window sizes the latent space is degenerate, and variability is observed only in the direction of a single latent variable that can be identified with the presence of domain wall. For larger window sizes, the latent space is 2D, and the disentangled latent variables can be generally interpreted as the degree of switching and complexity of domain structure. Applied to multiple consecutive PFM images acquired while monitoring domain switching, the polarization switching mechanism can thus be visualized in the latent space, providing insight into domain evolution mechanisms and their correlation with the microstructure.