Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-23T16:41:07.396Z Has data issue: false hasContentIssue false
  • English
  • Français

Causal Analysis of Parameterized Atomic HAADF-STEM Across a Doped Ferroelectric Phase Boundary

Published online by Cambridge University Press:  30 July 2021

Christopher Nelson ,
Maxim Ziatdinov ,
Xiaohang Zhang ,
Rama Vasudevan ,
Eugen Eliseev ,
Anna Morozovska ,
Ichiro Takeuchi  and
Sergei Kalinin
Christopher Nelson
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Maxim Ziatdinov
Affiliation:
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Xiaohang Zhang
Affiliation:
3University of Maryland, Maryland, United States
Rama Vasudevan
Affiliation:
Oak Ridge National Laboratory, United States
Eugen Eliseev
Affiliation:
Institute for Problems of Materials Science, National Academy of Sciences of Ukraine, United States
Anna Morozovska
Affiliation:
Institute of Physics, National Academy of Sciences of Ukraine, United States
Ichiro Takeuchi
Affiliation:
3University of Maryland, Maryland, United States
Sergei Kalinin
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
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. 2762 - 2764
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Daniusis, P. et al. , Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI) 7 (2010), 1.Google Scholar
Shimizu, S. et al. , Mach. Learn. Res. 7 (2006), p. 2003Google 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