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Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling
by
Li, Q.
, Li, L.-L.
, Nelson, C. T.
, Hsu, S.-L.
, Yadav, A. K.
, Ramesh, R.
, McCarter, M.
, Martin, L. W.
, Kalinin, S. V.
, Damodaran, A. R.
in
639/301/1034/1036
/ 639/301/357/537
/ 639/766/119/996
/ Atomic structure
/ Coarse-grained models
/ Computer vision
/ Electric polarization
/ Electron microscopy
/ Ferroelectrics and multiferroics
/ Humanities and Social Sciences
/ Learning algorithms
/ Machine learning
/ MATERIALS SCIENCE
/ Modelling
/ multidisciplinary
/ Polarization
/ Science
/ Science (multidisciplinary)
/ Strain
/ Strontium titanates
/ Structural properties
/ Superlattices
/ Vortices
2017
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Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling
by
Li, Q.
, Li, L.-L.
, Nelson, C. T.
, Hsu, S.-L.
, Yadav, A. K.
, Ramesh, R.
, McCarter, M.
, Martin, L. W.
, Kalinin, S. V.
, Damodaran, A. R.
in
639/301/1034/1036
/ 639/301/357/537
/ 639/766/119/996
/ Atomic structure
/ Coarse-grained models
/ Computer vision
/ Electric polarization
/ Electron microscopy
/ Ferroelectrics and multiferroics
/ Humanities and Social Sciences
/ Learning algorithms
/ Machine learning
/ MATERIALS SCIENCE
/ Modelling
/ multidisciplinary
/ Polarization
/ Science
/ Science (multidisciplinary)
/ Strain
/ Strontium titanates
/ Structural properties
/ Superlattices
/ Vortices
2017
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Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling
by
Li, Q.
, Li, L.-L.
, Nelson, C. T.
, Hsu, S.-L.
, Yadav, A. K.
, Ramesh, R.
, McCarter, M.
, Martin, L. W.
, Kalinin, S. V.
, Damodaran, A. R.
in
639/301/1034/1036
/ 639/301/357/537
/ 639/766/119/996
/ Atomic structure
/ Coarse-grained models
/ Computer vision
/ Electric polarization
/ Electron microscopy
/ Ferroelectrics and multiferroics
/ Humanities and Social Sciences
/ Learning algorithms
/ Machine learning
/ MATERIALS SCIENCE
/ Modelling
/ multidisciplinary
/ Polarization
/ Science
/ Science (multidisciplinary)
/ Strain
/ Strontium titanates
/ Structural properties
/ Superlattices
/ Vortices
2017
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Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling
Journal Article
Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling
2017
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Overview
Flexoelectricity refers to electric polarization generated by heterogeneous mechanical strains, namely strain gradients, in materials of arbitrary crystal symmetries. Despite more than 50 years of work on this effect, an accurate identification of its coupling strength remains an experimental challenge for most materials, which impedes its wide recognition. Here, we show the presence of flexoelectricity in the recently discovered polar vortices in PbTiO
3
/SrTiO
3
superlattices based on a combination of machine-learning analysis of the atomic-scale electron microscopy imaging data and phenomenological phase-field modeling. By scrutinizing the influence of flexocoupling on the global vortex structure, we match theory and experiment using computer vision methodologies to determine the flexoelectric coefficients for PbTiO
3
and SrTiO
3
. Our findings highlight the inherent, nontrivial role of flexoelectricity in the generation of emergent complex polarization morphologies and demonstrate a viable approach to delineating this effect, conducive to the deeper exploration of both topics.
Flexoelectric coupling between strain gradients and polarization influences the physics of ferroelectric devices but it is difficult to directly probe its effects. Here, Li et al. use principal component analysis to compare STEM images with phase-field modeling and extract the flexoelectric contributions.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
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