Toward Decoding the Relationship between Domain Structure and Functionality in Ferroelectrics via Hidden Latent Variables

TL;DR

This paper uses convolutional encoder-decoder networks to analyze the relationship between local domain structures and polarization switching in ferroelectrics, revealing insights into underlying physical mechanisms and identifying regions for detailed study.

Contribution

It introduces a novel machine learning workflow that correlates local spectral responses with domain structures, providing physical insights and a universal approach for spectral imaging analysis.

Findings

Latent variables reveal predictability of polarization switching

Regions with low predictability highlight targets for detailed studies

Workflow applicable to various spectral imaging techniques

Abstract

Polarization switching mechanisms in ferroelectric materials are fundamentally linked to local domain structure and presence of the structural defects, which both can act as nucleation and pinning centers and create local electrostatic and mechanical depolarization fields affecting wall dynamics. However, the general correlative mechanisms between domain structure and polarization dynamics are only weakly explored, precluding insight into the associated physical mechanisms. Here, the correlation between local domain structures and switching behavior in ferroelectric materials is explored using the convolutional encoder-decoder networks, enabling the image to spectral (im2spec) and spectral to image (spec2im) translations via encoding latent variables. The latter reflects the assumption that the relationship between domain structure and polarization switching is parsimonious, i.e. is based upon a small number of local mechanisms. The analysis of latent variables distributions and their real space representations provides insight into the predictability of the local switching behavior, and hence associated physical mechanisms. We further pose that the regions where these correlative relationships are violated, i.e. predictability of the polarization dynamics from domain structure is reduced, represent the obvious target for detailed studies, e.g. in the context of automated experiments. This approach provides a workflow to establish the presence of correlation between local spectral responses and local structure and can be universally applied to spectral imaging techniques such as PFM, scanning tunneling microscopy (STM) and spectroscopy, and electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM).