Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling

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 contribu...

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Autores principales: Q. Li, C. T. Nelson, S.-L. Hsu, A. R. Damodaran, L.-L. Li, A. K. Yadav, M. McCarter, L. W. Martin, R. Ramesh, S. V. Kalinin
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Lenguaje:EN
Publicado: Nature Portfolio 2017
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Acceso en línea:https://doaj.org/article/5c2afb0ab0ce462a92da7d13496d55bb
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spelling oai:doaj.org-article:5c2afb0ab0ce462a92da7d13496d55bb2021-12-02T14:42:34ZQuantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling10.1038/s41467-017-01733-82041-1723https://doaj.org/article/5c2afb0ab0ce462a92da7d13496d55bb2017-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-017-01733-8https://doaj.org/toc/2041-1723Flexoelectric 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.Q. LiC. T. NelsonS.-L. HsuA. R. DamodaranL.-L. LiA. K. YadavM. McCarterL. W. MartinR. RameshS. V. KalininNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-8 (2017)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Q. Li
C. T. Nelson
S.-L. Hsu
A. R. Damodaran
L.-L. Li
A. K. Yadav
M. McCarter
L. W. Martin
R. Ramesh
S. V. Kalinin
Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling
description 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.
format article
author Q. Li
C. T. Nelson
S.-L. Hsu
A. R. Damodaran
L.-L. Li
A. K. Yadav
M. McCarter
L. W. Martin
R. Ramesh
S. V. Kalinin
author_facet Q. Li
C. T. Nelson
S.-L. Hsu
A. R. Damodaran
L.-L. Li
A. K. Yadav
M. McCarter
L. W. Martin
R. Ramesh
S. V. Kalinin
author_sort Q. Li
title Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling
title_short Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling
title_full Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling
title_fullStr Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling
title_full_unstemmed Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling
title_sort quantification of flexoelectricity in pbtio3/srtio3 superlattice polar vortices using machine learning and phase-field modeling
publisher Nature Portfolio
publishDate 2017
url https://doaj.org/article/5c2afb0ab0ce462a92da7d13496d55bb
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