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Automatic detection of crystallographic defects in STEM images by unsupervised learning with translational invariance

Published online by Cambridge University Press:  30 July 2021

Yueming Guo ,
Andrew R. Lupini ,
Hui Cai ,
Kai Xiao ,
Sergiy Krylyuk ,
Albert Davydov ,
Qianying Guo  and
Sergei Kalinin
Yueming Guo
Affiliation:
Oak Ridge National Laboratory, OAK RIDGE, Tennessee, United States
Andrew R. Lupini
Affiliation:
Oak Ridge National Laboratory, United States
Hui Cai
Affiliation:
University of California, Merced, United States
Kai Xiao
Affiliation:
Oak Ridge National Laboratory, United States
Sergiy Krylyuk
Affiliation:
4National Institue of Standards and Technology, United States
Albert Davydov
Affiliation:
4National Institue of Standards and Technology, United States
Qianying Guo
Affiliation:
Oak Ridge National Laboratory, 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
Defects in Materials: How We See and Understand Them
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 1460 - 1462
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

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Work supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division, and performed at the Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy Office of Science User Facility.Google Scholar