Disentangling Ferroelectric Wall Dynamics and Identification of Pinning Mechanisms via Deep Learning

Yongtao Liu, Roger Proksch, Chun Yin Wong, Maxim Ziatdinov, Sergei V. Kalinin

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Field-induced domain-wall dynamics in ferroelectric materials underpins multiple applications ranging from actuators to information technology devices and necessitates a quantitative description of the associated mechanisms including giant electromechanical couplings, controlled nonlinearities, or low coercive voltages. While the advances in dynamic piezoresponse force microscopy measurements over the last two decades have rendered visualization of polarization dynamics relatively straightforward, the associated insights into the local mechanisms have been elusive. This work explores the domain dynamics in model polycrystalline materials using a workflow combining deep-learning-based segmentation of the domain structures with nonlinear dimensionality reduction using multilayer rotationally invariant autoencoders (rVAE). The former allows unambiguous identification and classification of the ferroelectric and ferroelastic domain walls. The rVAE discovers the latent representations of the domain wall geometries and their dynamics, thus providing insight into the intrinsic mechanisms of polarization switching, that can further be compared to simple physical models. The rVAE disentangles the factors affecting the pinning efficiency of ferroelectric walls, offering insights into the correlation of ferroelastic wall distribution and ferroelectric wall pinning.

Original languageEnglish
Article number2103680
JournalAdvanced Materials
Volume33
Issue number43
DOIs
StatePublished - Oct 28 2021

Funding

This effort (ML) is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Energy Frontier Research Centers program under Award Number DE‐SC0021118 (Y.L. and S.V.K.), and the Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a US Department of Energy, Office of Science User Facility (M.Z.).

Keywords

  • deep learning
  • domain wall dynamics
  • ferroelectrics
  • pinning mechanism

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