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Bayesian Inference for Materials Physics from STEM Data: The Probability Distribution of Physical Parameters from Ferroelectric Domain Wall Observations

Published online by Cambridge University Press:  30 July 2021

Christopher Nelson ,
Rama Vasudevan ,
Xiaohang Zhang ,
Maxim Ziatdinov ,
Eugen Eliseev ,
Ichiro Takeuchi ,
Anna Morozovska  and
Sergei Kalinin
Christopher Nelson
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Rama Vasudevan
Affiliation:
Oak Ridge National Laboratory, United States
Xiaohang Zhang
Affiliation:
University of Maryland, Maryland, United States
Maxim Ziatdinov
Affiliation:
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Eugen Eliseev
Affiliation:
Institute for Problems of Materials Science, National Academy of Sciences of Ukraine, United States
Ichiro Takeuchi
Affiliation:
University of Maryland, Maryland, United States
Anna Morozovska
Affiliation:
Institute of Physics, National Academy of Sciences of Ukraine, 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
Quantum Materials Probed by High Spatial and Energy Resolution in Scanning/Transmission Electron Microscopy
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 1212 - 1214
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Jia, C. L. et al. , Nature Materials 6 (2007), p. 64.CrossRefGoogle Scholar
Liu, Y., et al. , J. Mater. Res. 32 (2017), p. 847.Google Scholar
This work was supported by the U.S Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division.Google Scholar