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Direct mapping of polarization fields from STEM images: A Deep Learning based exploration of ferroelectrics

Published online by Cambridge University Press:  30 July 2021

Ayana Ghosh ,
Christopher Nelson ,
Mark Oxley ,
Xiaohang Zhang ,
Maxim Ziatdinov ,
Ichiro Takeuchi  and
Sergei Kalinin
Ayana Ghosh
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Christopher Nelson
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Mark Oxley
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Xiaohang Zhang
Affiliation:
University of Maryland, Maryland, United States
Maxim Ziatdinov
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Ichiro Takeuchi
Affiliation:
University of Maryland, Maryland, United States
Sergei Kalinin
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States

Abstract

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Type
Full System and Workflow Automation for Enabling Big Data and Machine Learning in Electron Microscopy
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 2990 - 2992
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Zubko, P. et al. , In Annual Review of Condensed Matter Physics, Vol 2, Langer, J. S., Ed. Annual Reviews: Palo Alto, 2011; Vol. 2, pp 141-165.Google Scholar
Chisholm, M. F. et al. , Atomic-Scale Compensation Phenomena at Polar Interfaces. Phys. Rev. Lett. 2010, 105 (19).CrossRefGoogle ScholarPubMed
Chang, H. J. et al. , Atomically Resolved Mapping of Polarization and Electric Fields Across Ferroelectric/Oxide Interfaces by Z-contrast Imaging. Adv. Mater. 2011, 23 (21), 2474-+.CrossRefGoogle ScholarPubMed
Borisevich, A. Y. et al. , Interface dipole between two metallic oxides caused by localized oxygen vacancies. Phys. Rev. B 2012, 86 (14).CrossRefGoogle Scholar
Borisevich, A. Y. et al. , Exploring Mesoscopic Physics of Vacancy-Ordered Systems through Atomic Scale Observations of Topological Defects. Phys. Rev. Lett. 2012, 109 (6).Google ScholarPubMed
Li, Q. et al. , Quantification of flexoelectricity in PbTiO3/SrTiO3 superlattice polar vortices using machine learning and phase-field modeling. Nat. Commun. 2017, 8.Google ScholarPubMed
Ziatdinov, M. et al. , Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data. npj Comput. Mater. 2020, 6 (1), 9.CrossRefGoogle Scholar
Ziatdinov, M. et al. , Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations. ACS Nano 2017, 11 (12), 12742-12752.Google Scholar