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From Event Detection to Physical Hypothesis Learning via Automated and Autonomous Microscopy

Published online by Cambridge University Press:  22 July 2022

Sergei V. Kalinin ,
Yongtao Liu ,
Rama Vasudevan ,
Kyle Kelley  and
Maxim Ziatdinov
Sergei V. Kalinin*
Affiliation:
Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, United States
Yongtao Liu
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, TN, United States
Rama Vasudevan
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, TN, United States
Kyle Kelley
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, TN, United States
Maxim Ziatdinov
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, TN, United States
*
*Corresponding author: [email protected]

Abstract

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Type
Correlative Microscopy and High-Throughput Characterization for Accelerated Development of Materials in Extreme Environments
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 2062 - 2063
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Copyright
Copyright © Microscopy Society of America 2022

References

Ovchinnikov, OS et al. , Microsc. Microanal. 16(S2) (2010). doi:10.1017/S1431927610062720CrossRefGoogle Scholar
Kalinin, SV et al. , ACS Nano 15(8) (2021), p. 12604. arXiv:2103.1216510.1021/acsnano.1c02104CrossRefGoogle Scholar
This work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Energy Frontier Research Centers program under Award Number DE-SC0021118. This work is conducted at the Center for Nanophase Materials Sciences, a US Department of Energy Office of Science User Facility.Google Scholar