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Exploring Local Crystal Symmetry with Rotationally Invariant Variational Autoencoders

Published online by Cambridge University Press:  22 July 2022

Mark P. Oxley*
Affiliation:
1. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United States
Sergei V. Kalinin
Affiliation:
1. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United States
Mani Valleti
Affiliation:
2. Bredesen Center for Interdisciplinary Research, The University of Tennessee, Knoxville, TN, United States
Junjie Zhang
Affiliation:
3.Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge TN, United States 6. State Key Laboratory of Crystal Materials & Institute of Crystal Materials, Shandong University, Jinan, Shandong, China
Raphael P. Hermann
Affiliation:
3.Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge TN, United States
Hong Zheng
Affiliation:
4. Materials Science Division, Argonne National Laboratory, Argonne, IL, United States
Wenrui Zhang
Affiliation:
1. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United States 3.Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge TN, United States 7. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang, China
Gyula Eres
Affiliation:
3.Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge TN, United States
Rama K. Vasudevan
Affiliation:
1. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United States
Maxim Ziatdinov
Affiliation:
1. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United States 5. Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
*
*Corresponding author: oxleymp@ornl.gov

Abstract

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Type
Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
Copyright
Copyright © Microscopy Society of America 2022

References

Ziatdinov, M et al. , Sci. Adv. 5 (2019). doi: 10.1126/sciadv.aaw8989CrossRefGoogle Scholar
Ziatdinov, M, AtomAI. GitHub repository, https://github.com/pycroscopy/atomai (2020).Google Scholar
Kalinin, SV, npj Comput Mater 7 (2021). https://doi.org/10.1038/s41524-021-00621-6Google Scholar
This effort (ML, STEM, film growth, sample growth) 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 (S.V.K., S.V., G.E., W.Z., J.Z., H.Z., R.P.H.) and was performed and partially supported (R.K.V., 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. Dr. Matthew Chisholm is gratefully acknowledged for the STEM data used in this work.Google Scholar
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