Chiral detection of biomolecules based on reinforcement learning
Yuxiang Chen, Fengyu Zhang, Zhibo Dang, Xiao He, Chunxiong Luo, Zhengchang Liu, Pu Peng, Yuchen Dai, Yijing Huang, Yu Li, Zheyu Fang
Abstract
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.