Yuxiang Chen 1†Fengyu Zhang 2,4†Zhibo Dang 1Xiao He 1[ ... ]Zheyu Fang 1,3,*
Author Affiliations
1 School of Physics, Peking University, Beijing 100871, China
2 The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics & Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
3 Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
4 Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China
Chirality plays an important role in biological processes, and enantiomers often possess similar physical properties and different physiologic functions. In recent years, chiral detection of enantiomers become a popular topic. Plasmonic metasurfaces enhance weak inherent chiral effects of biomolecules, so they are used in chiral detection. Artificial intelligence algorithm makes a lot of contribution to many aspects of nanophotonics. Here, we propose a nanostructure design method based on reinforcement learning and devise chiral nanostructures to distinguish enantiomers. The algorithm finds out the metallic nanostructures with a sharp peak in circular dichroism spectra and emphasizes the frequency shifts caused by nearfield interaction of nanostructures and biomolecules. Our work inspires universal and efficient machine-learning methods for nanophotonic design.
chiral detection metasurface deep learning cathodoluminescence 
Opto-Electronic Science
2023, 2(1): 220019

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