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)
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