Maximizing Modalities: Accelerating Quantitative Multimodal Electron Microscopy-Reference-Cited by-同舟云学术

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Maximizing Modalities: Accelerating Quantitative Multimodal Electron Microscopy

Published:2023-07-22 Issue:Supplement_1 Volume:29 Page:1868-1869
ISSN:1431-9276
Container-title:Microscopy and Microanalysis
language:en
Short-container-title:

Author:

Akers Sarah¹,Pope Jenna¹,Ter-Petrosyan Arman¹²,Matthews Bethany³,Paudel Rajendra⁴,Comes Ryan B⁴,Spurgeon Steven R³⁵

Affiliation:

1. National Security Directorate, Pacific Northwest National Laboratory , Richland, WA , United States

2. Department of Physics, University of California-Berkeley , Berkeley, CA , United States

3. Energy and Environment Directorate, Pacific Northwest National Laboratory , Richland, WA , United States

4. Department of Physics, Auburn University , Auburn, Alabama , United States

5. Department of Physics, University of Washington , Seattle, WA , United States

Publisher

Oxford University Press (OUP)

Subject

Instrumentation

Link

https://academic.oup.com/mam/article-pdf/29/Supplement_1/1868/50935973/ozad067.964.pdf

Reference6 articles.

1. Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication

2. Design of a graphical user interface for few-shot machine learning classification of electron microscopy data

3. The role of Nanocartography in the Development of Automated TEM

4. Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

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