Hostname: page-component-6b989bf9dc-lb7rp Total loading time: 0 Render date: 2024-04-14T12:43:05.660Z Has data issue: false hasContentIssue false

Real-time, On-Microscope Automated Quantification of Features in Microcopy Experiments Using Machine Learning and Edge Computing

Published online by Cambridge University Press:  22 July 2022

Kevin G. Field*
Affiliation:
University of Michigan – Ann Arbor, Ann Arbor, MI, United States Theia Scientific, LLC, Arlington, VA, United States
Priyam Patki
Affiliation:
University of Michigan – Ann Arbor, Ann Arbor, MI, United States
Nasir Sharaf
Affiliation:
Theia Scientific, LLC, Arlington, VA, United States
Kai Sun
Affiliation:
University of Michigan – Ann Arbor, Ann Arbor, MI, United States
Laura Hawkins
Affiliation:
Texas A&M University, College Station, TX, United States
Matthew Lynch
Affiliation:
University of Michigan – Ann Arbor, Ann Arbor, MI, United States
Ryan Jacobs
Affiliation:
University of Wisconsin – Madison, Madison, WI, United States
Dane D. Morgan
Affiliation:
University of Wisconsin – Madison, Madison, WI, United States
Lingfeng He
Affiliation:
Texas A&M University, College Station, TX, United States Idaho National Laboratory, Idaho Falls, ID, United States
Christopher R. Field
Affiliation:
Theia Scientific, LLC, Arlington, VA, United States
*
*Corresponding author: kgfield@umich.edu

Abstract

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Correlative Microscopy and High-Throughput Characterization for Accelerated Development of Materials in Extreme Environments
Copyright
Copyright © Microscopy Society of America 2022

References

Li, W, Field, KG and Morgan, D., Npj Computational Materials 4 (2018) p. 36. https://doi.org/10.1038/s41524-018-0093-8.CrossRefGoogle Scholar
Shen, M et al. , Computational Materials Science. 197 (2021) p. 110560. https://doi.org/10.1016/j.commatsci.2021.110560.CrossRefGoogle Scholar
Jacobs, R et al. , Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs, (2021). https://arxiv.org/abs/2110.08244v1 (accessed October 27, 2021).Google Scholar
Shen, M et al. , Computational Materials Science 199 (2021), p. 110576. https://doi.org/10.1016/J.COMMATSCI.2021.110576.CrossRefGoogle Scholar
Field, KG et al. , Microscopy and Microanalysis 27 (2021), p. 2136. https://doi.org/10.1017/S1431927621007704.CrossRefGoogle Scholar
Roberts, G et al. , Scientific Reports 9 (2019), p. 12744. https://doi.org/10.1038/s41598-019-49105-0.CrossRefGoogle Scholar
Anderson, CM et al. , Automated Classification of Helium Ingress in Irradiated X-750, ArXiv:1912.04252. (2019) p. 1.Google Scholar
Taller, S, Scime, L and Terrani, K, Microscopy and Microanalysis 27 (2021) p. 3110. https://doi.org/10.1017/S143192762101076X.CrossRefGoogle Scholar
Field, C. and Field, K., System and method for multi-modal microscopy, Provisional Patent Filed, 2021.Google Scholar
This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award Number DE-SC0021529 and U.S. Department of Energy, Office of Science, Office of Nuclear Energy under Award Number DE-SC0021936. Additional support for K.G.F., D.M. and R.J. was provided by Idaho National Laboratory as part of the Department of Energy (DOE) Office of Nuclear Energy, Nuclear Materials Discovery and Qualification Initiative (NMDQi). The authors would like to thank Dr. Yukinori Yamamoto of Oak Ridge National Laboratory for provision of the bulk sample used to develop microscopy samples for Fig. 1b-c.Google Scholar