Skip to main content Accessibility help
×
Home

Leveraging First Principles Modeling and Machine Learning for Microscopy Data Inversion

  • Eric Schwenker (a1), Fatih Sen (a1), Spencer Hills (a1), Tadas Pualauskas (a2), Ce Sun (a3), Liang Li (a1), Alper Kinaci (a1), Kendra Letchworth-Weaver (a1), Moon Kim (a3), Robert Klie (a2), Jianguo Wen (a1) and Maria K. Y. Chan (a1)...
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Leveraging First Principles Modeling and Machine Learning for Microscopy Data Inversion
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Leveraging First Principles Modeling and Machine Learning for Microscopy Data Inversion
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Leveraging First Principles Modeling and Machine Learning for Microscopy Data Inversion
      Available formats
      ×

Abstract

Copyright

References

Hide All
[1] Lowe, D. Proceedings of the International Conference on Computer Vision 2 1999). p. 1150.
[2] Dalal, N., Navneet, & Triggs, B. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1 2005). p. 886.
[3] Paulauskas, T., et al 43rd IEEE Photovoltaic Specialists Conference (PVSC) (2016).
[4] We acknowledge helpful discussions with Dane Morgan, Paul Voyles, Rebecca Willet, Simon Billinge, Amanda Petford-Long, and John Mitchell. Use of the Center of Nanoscale Materials, an Office of Science user facility, was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract no. DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. We gratefully acknowledge the computing resources provided on Blues, a high performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory.

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed