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Bayesian Microscopy: Model Selection for Extracting Weak Nonlinearities from Scanning Probe Microscopy Data

Published online by Cambridge University Press:  30 July 2020

Rama Vasudevan ,
Kyle Kelley ,
Eugene Eliseev ,
Hiroshi Funakubo ,
Stephen Jesse ,
Anna Morozovska  and
Sergei Kalinin
Rama Vasudevan
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Kyle Kelley
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Eugene Eliseev
Affiliation:
National Academy of Sciences of Ukraine, Kyiv, Kyyiv, Ukraine
Hiroshi Funakubo
Affiliation:
Tokyo Institute of Technology, Yokohama, Kanagawa, Japan
Stephen Jesse
Affiliation:
Oak Ridge National Laboratory, NA, Alabama, United States
Anna Morozovska
Affiliation:
National Academy of Sciences of Ukraine, Kyiv, Kyyiv, Ukraine
Sergei Kalinin
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States

Abstract

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Type
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Information
Microscopy and Microanalysis , Volume 26 , Supplement S2 , August 2020 , pp. 2126 - 2127
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Copyright
Copyright © Microscopy Society of America 2020

References

Jesse, S., Kalinin, S. V., Proksch, R., Baddorf, A. P. a Rodriguez, B. J., Nanotechnology 18 (2007), p. 43550310.1088/0957-4484/18/43/435503CrossRefGoogle Scholar
Watanabe, S., J. Mach. Learn. Res. 14 (2013), p. 867Google Scholar
This research was conducted at the Center for Nanophase Materials Sciences, which also provided support (R. K. V, S. J., S. V. K.) and is a US DOE Office of Science User Facility.Google Scholar