Accurate Passive 3D Polarization Face Reconstruction under Complex Conditions Assisted with Deep Learning

Research context

Research on electrical energy conversion, storage and generation dates back to the nineteenth century, but only in recent years have scientists begun to investigate the impact of electron spin on these processes. The ability to control and manipulate this intrinsically quantum property of matter opens new approaches to addressing energy science challenges. The chiral-induced spin selectivity (CISS) effect is central to this effort, as it enables control over the transport and generation of both pure spin currents and spin-polarized charge currents. In this Review, we first introduce design strategies for implementing CISS in materials and then describe examples of how CISS has been used to improve electrocatalysis and spintronics. We conclude with a forward-looking perspective on the next steps for leveraging CISS in energy science.

Keywords: Computer science, Artificial intelligence, Polarization (electrochemistry), Computer vision, Convolutional neural network, Face (sociological concept), Surface reconstruction, Iterative reconstruction, Pixel, Facial recognition system, 3D reconstruction, Deep learning, Optics, Pattern recognition (psychology), Surface (topology), Physics, Mathematics, Geometry, Sociology, Chemistry, Social science, Physical chemistry

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Bibliographic record reviewed for relevance and publication quality. Full-text findings have not been extracted; consult the original publication for methods and results.

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17 citations · OpenAlex · observed 2026-09-08

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Cite / 引用

Pingli Han, Xuan Li, Fei Liu, Yudong Cai, Kui Yang, Mingyu Yan, Shaojie Sun, Yanyan Liu, Xiaopeng Shao. Accurate Passive 3D Polarization Face Reconstruction under Complex Conditions Assisted with Deep Learning. Photonics (2022). https://doi.org/10.3390/photonics9120924

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