Hostname: page-component-848d4c4894-2xdlg Total loading time: 0 Render date: 2024-06-14T21:05:15.407Z Has data issue: false hasContentIssue false
  • English
  • Français

FerroNet: Machine Learning Flow for Analysis of Ferroelectric and Ferroelastic Materials

Published online by Cambridge University Press:  05 August 2019

Maxim Ziatdinov ,
Chris Nelson  and
Sergei V. Kalinin
Maxim Ziatdinov
Affiliation:
Computational Sciences & Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN. Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, TN.
Chris Nelson
Affiliation:
Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, TN. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN.
Sergei V. Kalinin
Affiliation:
Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, TN. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN.

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
Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy
Information
Microscopy and Microanalysis , Volume 25 , Supplement S2 , August 2019 , pp. 170 - 171
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]https://github.com/pycroscopy/AICrystallographer.Google Scholar
[2]The authors acknowledge funding from the US Department of Energy, Office of Science, Basic Energy Sciences, Division of Materials Science and Engineering (CN, SVK). The work was performed at the Center for Nanophase Materials Sciences, which is a US DOE Office of Science User Facility.Google Scholar