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

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

doi:10.1002/adma.202103680

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

Journal Article · Sun Sep 12 00:00:00 EDT 2021 · Advanced Materials

DOI:https://doi.org/10.1002/adma.202103680· OSTI ID:1866709

^[1]; Proksch, Roger ^[2]; Wong, Chun Yin ^[3]; ^[1]; ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Asylum Research, Santa Barbara, CA (United States)
Univ. of Tennessee, Knoxville, TN (United States)

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. Here, the rVAE discovers the latent representations of the domain wall geometries and their dynamics, consequently 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.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Pennsylvania State Univ., University Park, PA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC05-00OR22725; SC0021118

OSTI ID:: 1866709

Alternate ID(s):: OSTI ID: 1821018
OSTI ID: 1961620

Journal Information:: Advanced Materials, Journal Name: Advanced Materials Journal Issue: 43 Vol. 33; ISSN 0935-9648

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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