Individual Barkhausen Pulses of Ferroelastic Nanodomains

Kalinin

et al. 2022

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Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.

Section: Insights Into Phase Transitiomentioning

confidence: 78%

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations

Valleti

Kalinin

et al. 2022

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“…[ 40 ] The sample was heated to a temperature slightly under the Curie temperature where the 90° domains were stable and the response of the needle‐shaped nanodomains changes in the magnitude and direction of the induced electric field could be analyzed. [ 41 ]…”

Section: Resultsmentioning

confidence: 99%

“…Positive and negative voltages were applied to a thin lamella sample using a triangular waveform in the polarization direction; the thin BaTiO 3 sample was mounted on a microelectromechanical system-based device secured in a TEM holder. [40] The sample was heated to a temperature slightly under the Curie temperature where the 90° domains were stable and the response of the needle-shaped nanodomains changes in the magnitude and direction of the induced electric field could be analyzed. [41] Figure 1 shows a series of bright-field (BF) STEM images that illustrate the evolution of the domain structure in BaTiO 3 during polarization switching at 130 °C.…”

Section: Resultsmentioning

confidence: 99%

“…[40] The sample was heated to a temperature slightly under the Curie temperature where the 90° domains were stable and the response of the needle-shaped nanodomains changes in the magnitude and direction of the induced electric field could be analyzed. [41] Figure 1 shows a series of bright-field (BF) STEM images that illustrate the evolution of the domain structure in BaTiO 3 during polarization switching at 130 °C. The lines of dark contrast in the images correspond to domain walls; the random distribution of dark spots correspond to localized areas of built-up contamination on the BaTiO 3 sample.…”

Section: Resultsmentioning

confidence: 99%

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Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

Ziatdinov

Vasudevan

et al. 2022

Adv Funct Materials

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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. 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.

“…For materials science, the observation of devices and materials at various temperatures, under an external stimulus (electrical, magnetic, light, etc. ), or ideally with a combination of both is still challenging. ,− …”

Section: Challenges To In Situ and Operando Tem Experiments At Low Te...mentioning

confidence: 99%

Challenges and Applications to Operando and In Situ TEM Imaging and Spectroscopic Capabilities in a Cryogenic Temperature Range

Tyukalova¹,

Vas²,

et al. 2021

Acc. Chem. Res.

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