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Unsupervised Machine Learning to Distill Structural-Property Insights from 4D-STEM

Published online by Cambridge University Press:  05 August 2019

Xin Li
Affiliation:
Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, TN Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN
Ondrej Dyck
Affiliation:
Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, TN Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN
Mark Oxley
Affiliation:
Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, TN Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN
Andrew Lupini
Affiliation:
Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, TN Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN
Stephen Jesse
Affiliation:
Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, TN Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN
Sergei V. Kalinin
Affiliation:
Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, TN Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN

Abstract

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Type
Advances in Phase Retrieval Microscopy
Copyright
Copyright © Microscopy Society of America 2019 

References

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[8]Research was partially supported at the Center for Nanophase Materials Sciences, which is a US DOE Office of Science User Facility. Part of this work was supported by the Office of Basic Energy Sciences, Materials Sciences and Engineering Division, US Department of Energy.Google Scholar