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Machine Learning for Atomic Scale Chemical and Morphological Assessment

Published online by Cambridge University Press:  01 August 2018

Scott Broderick ,
Tianmu Zhang ,
Bhargava Urala Kota ,
Ramachandran Subramanian ,
Srirangaraj Setlur ,
Venu Govindaraju  and
Krishna Rajan
Scott Broderick
Affiliation:
Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY
Tianmu Zhang
Affiliation:
Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY
Bhargava Urala Kota
Affiliation:
Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY
Ramachandran Subramanian
Affiliation:
Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY
Srirangaraj Setlur
Affiliation:
Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY
Venu Govindaraju
Affiliation:
Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY
Krishna Rajan
Affiliation:
Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY

Abstract

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Abstract
Information
Microscopy and Microanalysis , Volume 24 , Supplement S1: Proceedings of Microscopy & Microanalysis 2018 , August 2018 , pp. 526 - 527
Copyright
© Microscopy Society of America 2018 

References

[1] Zhang, T., Broderick, S.R. Rajan, K. In Nanoinformatics (ed. I. Tanaka Springer. p. 133.Google Scholar
[2] Loyola, C., et al, Journal of Vacuum Science and Technology A 34 2016) p. 061404.Google Scholar
[3] Broderick, S.R., et al, Ultramicroscopy 132 2013) p. 121.Google Scholar
[4] We acknowledge support from NSF DIBBs project award number ARI 1640867.Google Scholar