AI for nanoparticle TEM/STEM has progressed from detection and segmentation to physics-informed restoration, 2D-to-3D inference, and spatiotemporal analysis of in situ dynamics, with remaining gaps in benchmarking and ground truth.
Review: Deep Learning in Electron Microscopy
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abstract
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Afterwards, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.
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cond-mat.mtrl-sci 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy
AI for nanoparticle TEM/STEM has progressed from detection and segmentation to physics-informed restoration, 2D-to-3D inference, and spatiotemporal analysis of in situ dynamics, with remaining gaps in benchmarking and ground truth.