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Machine Learning Enabled Reproducible Data Analysis for Electron Microscopy

Published online by Cambridge University Press:  22 July 2022

Xiaoting Zhong ,
Nestor J. Zaluzec ,
Yu Lin  and
Jiadong Gong
Xiaoting Zhong
Affiliation:
Materials Science Department, Lawrence Livermore National Laboratory, Livermore, CA, USA
Nestor J. Zaluzec
Affiliation:
Photon Science Directorate, Argonne National Laboratory, Lemont, IL, USA
Yu Lin*
Affiliation:
QuesTek Innovations LLC, Evanston, IL, USA
Jiadong Gong
Affiliation:
QuesTek Innovations LLC, Evanston, IL, USA
*
*Corresponding author: ylin@questek.com

Abstract

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Type
On Demand - Artificial Intelligence, Instrument Automation, and High-Dimensional Data Analytics for Microscopy and Microanalysis
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 3138 - 3140
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Copyright
Copyright © Microscopy Society of America 2022

References

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Lin, T. Y., et al. , In European conference on computer vision (2014), p. 740-755.Google Scholar
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This work is supported by the Office of Science of the US Department of Energy under STTR award DE-SC0021563; as well as the Photon Science Directorate at Argonne National Laboratory, by the Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This work was also performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was supported by the LLNL-LDRD Program under Project No. 19-SI-001.Google Scholar