Hostname: page-component-7479d7b7d-fwgfc Total loading time: 0 Render date: 2024-07-10T14:32:21.880Z Has data issue: false hasContentIssue false
  • English
  • Français

A Natural Basis for Unsupervised Machine Learning on Scanning Diffraction Data

Published online by Cambridge University Press:  01 August 2018

Paul Cueva ,
Elliot Padget  and
David A. Muller
Paul Cueva
Affiliation:
School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA
Elliot Padget
Affiliation:
School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA
David A. Muller
Affiliation:
School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA Kavli Institute at Cornell for Nanoscale Science, Ithaca, NY, USA

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Abstract
Information
Microscopy and Microanalysis , Volume 24 , Supplement S1: Proceedings of Microscopy & Microanalysis 2018 , August 2018 , pp. 490 - 491
Copyright
© Microscopy Society of America 2018 

References

[1] Tate, M, et al., Microscopy and Microanalysis 22 2016) pp. 237–249.Google Scholar
[2] Belianinov, A, et al., Advanced Structural and Chemical Imaging 1(1 2015) p. 6.Google Scholar
[3] Xu, W LeBeau, J Microscopy and Microanalysis 23(S1 2017) pp: 120–121.Google Scholar
[4] PC was supported by the Center for Bright Beams, an NSF STC (PHY-1549132). This work made use of the Cornell Center for Materials Research Shared Facilities, an NSF MRSEC (DMR-1719875 and NSF-MRI-1429155). EP supported by an NSF Graduate Research Fellowship (DGE-1650441).Google Scholar