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A Semi-Supervised Machine Learning Workflow to Extract Quantitative Insights From Ultrafast Electron Microscopy Datasets

Published online by Cambridge University Press:  22 July 2022

Arun Baskaran ,
Faran Zhou ,
Thomas E. Gage ,
Haihua Liu ,
Ilke Arslan ,
Haidan Wen  and
Maria K.Y. Chan
Arun Baskaran*
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
Faran Zhou
Affiliation:
X-ray Science Division, Argonne National Laboratory, Lemont, IL, USA
Thomas E. Gage
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
Haihua Liu
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
Ilke Arslan
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
Haidan Wen
Affiliation:
X-ray Science Division, Argonne National Laboratory, Lemont, IL, USA Material Science Division, Argonne National Laboratory, Lemont, IL, USA
Maria K.Y. Chan*
Affiliation:
Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
*
*Corresponding author: abaskaran@anl.gov, mchan@anl.gov
*Corresponding author: abaskaran@anl.gov, mchan@anl.gov

Abstract

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Type
Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 3028 - 3030
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Copyright
Copyright © Microscopy Society of America 2022

References

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This work was performed at the Center for Nanoscale Materials, a U.S. Department of Energy Office of Science User Facility, and supported by the U.S. Department of Energy, Office of Science, under Contract No. DE-AC02-06CH11357. In addition, this research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory.Google Scholar