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High-Throughput Identification of Crystal Structures Via Machine Learning

Published online by Cambridge University Press:  05 August 2019

Kevin Kaufmann ,
Chaoyi Zhu ,
Alexander S. Rosengarten ,
Daniel Maryanovsky ,
Tyler Harrington ,
Eduardo Marin  and
Kenneth Vecchio
Kevin Kaufmann
Affiliation:
Department of NanoEngineering, UC San Diego, La Jolla, CA, USA.
Chaoyi Zhu
Affiliation:
Materials Science and Engineering Program, UC San Diego, La Jolla, CA, USA.
Alexander S. Rosengarten
Affiliation:
Department of NanoEngineering, UC San Diego, La Jolla, CA, USA.
Daniel Maryanovsky
Affiliation:
Department of Cognitive Science, UC San Diego, La Jolla, CA, USA.
Tyler Harrington
Affiliation:
Materials Science and Engineering Program, UC San Diego, La Jolla, CA, USA.
Eduardo Marin
Affiliation:
Department of NanoEngineering, UC San Diego, La Jolla, CA, USA.
Kenneth Vecchio*
Affiliation:
Department of NanoEngineering, UC San Diego, La Jolla, CA, USA. Materials Science and Engineering Program, UC San Diego, La Jolla, CA, USA.
*
*Corresponding author: [email protected]

Abstract

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Type
The Success of TMBA: TEM and STEM Developments in Techniques, Applications and Education
Information
Microscopy and Microanalysis , Volume 25 , Supplement S2 , August 2019 , pp. 2258 - 2259
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Walz, T et al. , Nature 387 (1997), p. 624.Google Scholar
[2]Curtarolo, S et al. , Phys. Rev. Lett. 91 (2003), p. 135503.Google Scholar
[3]Cowley, JM et al. , International Tables for Crystallography (2006), p. 276. doi:10.1107/97809553602060000558Google Scholar
[4]LeCun, Y, Bengio, Y, and Hinton, G, Nature 521 (2015), p. 436.Google Scholar
[5]K. Kaufmann was supported by the Department of Defense (DoD) through the National Defense Science and Engineering Graduate Fellowship (NDSEG) Program. K. Kaufmann would also like to acknowledge the support of the ARCS Foundation.Google Scholar