4.6 Article

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

期刊

NANOTECHNOLOGY
卷 33, 期 5, 页码 -

出版社

IOP Publishing Ltd
DOI: 10.1088/1361-6528/ac2f5b

关键词

scanning probe microscopy; machine learning; neural networks; ferroelectrics; latent space models

资金

  1. Center for Nanophase Materials Sciences
  2. U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Energy Frontier Research Centers program [DE-SC0021118]
  3. NSF (MRI development award) [DMR-1726862]

向作者/读者索取更多资源

In this study, domain switching pathways in ferroelectric materials were explored using a variational autoencoder, allowing for the visualization of domain evolution mechanisms and their correlation with the microstructure.
Domain switching pathways in ferroelectric materials visualized by dynamic piezoresponse force microscopy (PFM) are explored via variational autoencoder, which simplifies the elements of the observed domain structure, crucially allowing for rotational invariance, thereby reducing the variability of local polarization distributions to a small number of latent variables. For small sampling window sizes the latent space is degenerate, and variability is observed only in the direction of a single latent variable that can be identified with the presence of domain wall. For larger window sizes, the latent space is 2D, and the disentangled latent variables can be generally interpreted as the degree of switching and complexity of domain structure. Applied to multiple consecutive PFM images acquired while monitoring domain switching, the polarization switching mechanism can thus be visualized in the latent space, providing insight into domain evolution mechanisms and their correlation with the microstructure.

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