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Probing atomic-scale symmetry breaking by rotationally invariant machine learning of 4D-STEM Data.

Published online by Cambridge University Press:  30 July 2021

Mark Oxley ,
Maxim Ziatdinov ,
Ondrej Dyck ,
Andrew R. Lupini ,
Rama Vasudevan  and
Sergei Kalinin
Mark Oxley
Affiliation:
Center for Nanophase Materials Sciences, 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
Ondrej Dyck
Affiliation:
Oak Ridge National Laboratory, United States
Andrew R. Lupini
Affiliation:
Oak Ridge National Laboratory, United States
Rama Vasudevan
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
Diffraction Imaging Across Disciplines
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 2200 - 2201
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Müller, K. et al. Nature communications 5 (2014), p. 1.Google Scholar
Close, R. et al. , Ultramicroscopy 159 (2015), p. 124.CrossRefGoogle Scholar
This effort (ML and STEM) is based upon work supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (M.P.O., A.R.L., S.V.K., O.D.) and was performed and partially supported (M.Z.) at the Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility. This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.Google Scholar