Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

Ignatans, Reinis; Ziatdinov, Maxim; Vasudevan, Rama K.; Valleti, Sai Mani Prudhvi; Tileli, Vasiliki; Kalinin, Sergei V.

doi:10.1002/adfm.202100271

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

Journal Article · Thu Mar 03 23:00:00 EST 2022 · Advanced Functional Materials

DOI:https://doi.org/10.1002/adfm.202100271· OSTI ID:2326322

Ignatans, Reinis ^[1]; ^[2]; ^[2]; Valleti, Sai Mani Prudhvi ^[3]; Tileli, Vasiliki ^[1]; ^[2]

Ecole Polytechnique Federale Lausanne (EPFL) (Switzerland)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
Univ. of Tennessee, Knoxville, TN (United States). Bredesen Center for Interdisciplinary Research and Graduate Education

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data sets has remained a problem due to a large variety of possible configurations, many of which are degenerate. Here, an approach based on a combination of deep learning-based semantic segmentation, rotationally invariant variational autoencoder (VAE), and non-negative matrix factorization to enable learning of a latent space representation of the data with multiple real-space rotationally equivalent variants mapped to the same latent space descriptors is introduced. By varying the size of training sub-images in the VAE, the degree of complexity in the structural descriptors is tuned from simple domain wall detection to the identification of switching pathways. Importantly, this yields a powerful tool for the exploration of the dynamic data in mesoscopic electron, scanning probe, optical, and chemical imaging. Moreover, this work adds to the growing body of knowledge of incorporating physical constraints into the machine and deep-learning methods to improve learned descriptors of physical phenomena.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2326322

Alternate ID(s):: OSTI ID: 1847423

Journal Information:: Advanced Functional Materials, Journal Name: Advanced Functional Materials Journal Issue: 23 Vol. 32; ISSN 1616-301X

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

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