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Simulation-Trained Machine Learning Models for Lorentz Microscopy

Published online by Cambridge University Press:  22 July 2022

Arthur R. C. McCray ,
Amanda K. Petford-Long  and
Charudatta Phatak
Arthur R. C. McCray*
Affiliation:
Materials Science Division, Argonne National Laboratory, Lemont, IL, USA Applied Physics Program, Northwestern University, Evanston, IL, USA
Amanda K. Petford-Long
Affiliation:
Materials Science Division, Argonne National Laboratory, Lemont, IL, USA Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
Charudatta Phatak
Affiliation:
Materials Science Division, Argonne National Laboratory, Lemont, IL, USA
*
*Corresponding author: amccray@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. 2980 - 2982
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Copyright
Copyright © Microscopy Society of America 2022

References

Fert, A. et al. Nature Reviews Materials, 2 (2017), p. 17031. doi:10.1038/natrevmats.2017.31CrossRefGoogle Scholar
Loudon, J. C. et al. Advanced Materials, 31 (2019), p. 1806598. doi:10.1002/adma.201806598CrossRefGoogle Scholar
McCray, A. et al. Physical Review Applied, 15 (2021), p. 044025. doi:10.1103/PhysRevApplied.15.044025CrossRefGoogle Scholar
This work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division. Use of the Center for Nanoscale Materials, an Office of Science user facility, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.Google Scholar