Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images Article Swipe
Abid Khan
,
Chia‐Hao Lee
,
Pinshane Y. Huang
,
Bryan K. Clark
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-023-01042-3
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-023-01042-3
The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41524-023-01042-3
- https://www.nature.com/articles/s41524-023-01042-3.pdf
- OA Status
- gold
- Cited By
- 38
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378714594
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4378714594Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41524-023-01042-3Digital Object Identifier
- Title
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Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy imagesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-29Full publication date if available
- Authors
-
Abid Khan, Chia‐Hao Lee, Pinshane Y. Huang, Bryan K. ClarkList of authors in order
- Landing page
-
https://doi.org/10.1038/s41524-023-01042-3Publisher landing page
- PDF URL
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https://www.nature.com/articles/s41524-023-01042-3.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41524-023-01042-3.pdfDirect OA link when available
- Concepts
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Generative grammar, Adversarial system, Computer science, Generative adversarial network, Transmission electron microscopy, Artificial intelligence, Scanning electron microscope, Transmission (telecommunications), Materials science, Optics, Nanotechnology, Physics, Image (mathematics), TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
38Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 18, 2024: 17, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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