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 ...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.展开更多
基金This work is supported by the National Science Foundation of China(Grant Nos.12027807,62225501,and 11974002)National Key Research and Development Program of China(Grant No.2020YFA0211300,2020YFA0906900,and 2021YFF1200500)PKU-Baidu Fund Project(Grant No.2020BD023),and High-performance Computing Platform of Peking University.
文摘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.
