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Research Highlight

Disentangling Ferroelectric Wall Dynamics and Identifying Pinning Mechanisms via Deep Learning

Scientific Achievement

The deep learning workflow disentangles the factors affecting the pinning efficiency of ferroelectric walls, offering insights into the correlation of ferroelastic wall distribution and ferroelectric wall pinning.

Significance and Impact

The approach developed can be universally applied for assessing the time-dependent dynamics of complex materials. (Jupyter Notebook is available on Github)

Research Details

– A ResHED-Net is developed to generate domain wall (DW) maps from piezoresponse force microscopy images.
– The rotationally invariant variational autoencoder (rVAE) discovers the latent representations of DW geometries and their dynamics.  rVAE analysis of stacked ferroelectric and ferroelastic DW images discovered: (1) coincident locations of ferroelectric and ferroelastic walls and (2) asymmetric distribution of ferroelastic walls around ferroelectric walls.
 

Yongtau Liu, Roger Proksch, Chun Yin Wong, Maxim Ziatdinov, and Sergei V. Kalinin, "Disentangling Ferroelectric Wall Dynamics and Identification of Pinning Mechanisms via Deep Learning," Adv. Mater. 33, 2103680 (2021).  DOI: 10.1002/adma.202103680