Some peculiarities of 180°-domain wall moticn in thin ferroelectric crystals

Burtsev, E. V.; Chervonobrodov, S. Y.

doi:10.1080/07315178308202965

GFEL

1983

DOI: 10.1080/07315178308202965

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Some peculiarities of 180°-domain wall moticn in thin ferroelectric crystals

E. V. Burtsev¹,

S. Y. Chervonobrodov²

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1988

2021

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Cited by 4 publications

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“…While phase-field studies can be used to explore the domain structure evolution under the action of external fields, , realistic materials tend to contain defects and imperfections absent in phase-field models. Similarly, strong polarization-lattice pinning in ferroelectrics leading to Muller-Weinreich mechanisms for domain wall motion is difficult to represent via theoretical models. − In addition, the polarization dynamics on open ferroelectric surfaces is strongly affected by the ionic and internal screening. , …”

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Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

2021

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Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. This analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

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confidence: 99%

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

2021

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Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding

et al. 2021

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Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway toward exploring ferroelectric, ferroelastic, and chemical phenomena on the atomic scale. Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements. Here, we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks (DCNNs). In this approach, the DCNN is trained on the labeled part of the image (i.e., for human labelling), and the trained network is subsequently applied to other images. We explore the effects of the choice of the descriptors (centered on atomic columns and grid-based), the effects of observational bias, and whether the network trained on one composition can be applied to a different one. This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.

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Orientation instability of domain boundary in ferroelectrics

Chervonobrodov

Roytburd

1988

Ferroelectrics

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Some peculiarities of 180°-domain wall moticn in thin ferroelectric crystals

Cited by 4 publications

References 0 publications

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding

Orientation instability of domain boundary in ferroelectrics

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