Disentangling Ferroelectric Wall Dynamics and Identification of Pinning Mechanisms via Deep Learning. Issue 43 (12th September 2021)

• Record Type: Journal Article
• Authors: Liu, Yongtao; Proksch, Roger; Wong, Chun Yin; Ziatdinov, Maxim; Kalinin, Sergei V.
• Is Part Of: Advanced materials. Volume 33:Issue 43(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-12
• Subjects: deep learning -- domain wall dynamics -- ferroelectrics -- pinning mechanism; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202103680
• Languages: English
• ISSNs: 0935-9648

Abstract

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. Abstract : The domain dynamics in model polycrystalline materials is explored using a workflow combining deep‐learning‐based segmentation of the visualized domain structures and rotationally invariant autoencoders (rVAE). The former allows unambiguous identification and classification of the ferroelectric and ferroelastic domain walls. The rVAE stage further identifies intrinsic mechanisms of switching that can be compared to simple physical models.