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

Kalinin, Sergei V.; Steffes, James; Liu, Yongtao; Huey, Bryan D.; Ziatdinov, Maxim

doi:10.48550/arxiv.2007.06194

Cited by 1 publication

(4 citation statements)

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“…The third step of the workflow is unsupervised learning of lowdimensional representations of the data, realized here using a multilayer rotationally invariant autoencoder. [61] The individual elements of this workflow are discussed in detail below.…”

Section: Resultsmentioning

confidence: 99%

“…The second step of the workflow is feature engineering. Previously, we have introduced an approach where the features were image patches centered on a uniform spatial grid [61] and specific atomic location. [64] Here, we show that specific aspects of domain wall dynamics can be probed via selection of the patches centered at the specific domain wall types and introducing a time-delayed description.…”

Section: Resultsmentioning

confidence: 99%

“…Here, we explore the time-dependent domain wall dynamics in ferroelectric materials via latent representation of the timedependent data. [61][62][63] We propose the feature-engineering approach that effectively encodes time-dependent wall dynamics and demonstrate the applicability of rotationally invariant variational autoencoder to establish the salient features of the domain wall dynamics. The individual elements of this workflow as well as the integrated approach per se can be universally applied to the multitude of PFM applications.…”

Section: Introductionmentioning

confidence: 99%

“…The third step of the workflow is unsupervised learning of low dimensional representations of the data, realized here using a multilayer rotationally-invariant autoencoder. 48 The individual elements of this workflow are discussed in detail below. The first step of performing domain wall analysis is generating wall maps, which is a computational edge detection problem.…”

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confidence: 99%

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

et al. 2021

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%