PtyRANNOSAUR uses convolutional autoencoders trained on crystal structure databases to map 4D-STEM ptychography data to sub-0.5 Å phase images 10-100x faster than iterative methods while handling partial coherence, multiple scattering, and scan errors.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cond-mat.mtrl-sci 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Thesis reviews ptychography algorithms for 4D STEM and demonstrates SSB reconstruction on simulated MoS2 monolayer data.
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PtyRANNOSAUR: Ptychography with Robust Artificial Neural Networks Optimized for Sub-Angstrom Accuracy and Ultrafast Reconstruction
PtyRANNOSAUR uses convolutional autoencoders trained on crystal structure databases to map 4D-STEM ptychography data to sub-0.5 Å phase images 10-100x faster than iterative methods while handling partial coherence, multiple scattering, and scan errors.
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Ptychographic Algorithms for Phase Recovery in 4D Scanning Transmission Electron Microscopy
Thesis reviews ptychography algorithms for 4D STEM and demonstrates SSB reconstruction on simulated MoS2 monolayer data.