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Microscopy and Microanalysis Microscopy and Microanalysis

Article contents

Machine Learning for High Throughput HRTEM Analysis

Published online by Cambridge University Press:  05 August 2019

Catherine Groschner ,
Christina Choi ,
Dat Nguyen ,
Colin Ophus  and
Mary Scott
Catherine Groschner
Affiliation:
Department of Materials Science and Engineering, UC Berkeley, Berkeley, CA
Christina Choi
Affiliation:
Department of Materials Science and Engineering, UC Berkeley, Berkeley, CA
Dat Nguyen
Affiliation:
Department of Materials Science and Engineering, UC Berkeley, Berkeley, CA
Colin Ophus
Affiliation:
Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, Berkeley, CA
Mary Scott
Affiliation:
Department of Materials Science and Engineering, UC Berkeley, Berkeley, CA Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, Berkeley, CA
Corresponding
E-mail address:
cgroschner@berkeley.edu
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Type
Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy
Information
Microscopy and Microanalysis , Volume 25 , Supplement S2 , August 2019 , pp. 150 - 151
Copyright
Copyright © Microscopy Society of America 2019 

References

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[3]Shin, H. C. et al. IEEE Trans. Med. Imaging 35 (2016) p. 1285–1298.CrossRefGoogle Scholar
[4]Krizhevsky, A., Sutskever, I. & Hinton, G. E. Adv. Neural Inf. Process. Syst. (2012) p.1–9.Google Scholar
[5]Szegedy, C. Nips (2013) p. 1-9.Google Scholar
[6]Ziatdinov, M. et al. ACS Nano 11 (2017) p. 12742–12752.CrossRefGoogle Scholar
[7]Madsen, J. et al. Adv. Theory Simul. 1800037 (2018) p. 1–12.Google Scholar
[8]Kirkland, E. J. Advanced Computing in Electron Microscopy, (Springer, New York).Google Scholar
[9]Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This work is supported by the NSF GRFP under Grant No. 1752814 and by STROBE: NSF-STC (DMR-1548294).Google Scholar

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