(Peer-Reviewed) Chiral detection of biomolecules based on reinforcement learning
Yuxiang Chen 陈宇翔 ¹, Fengyu Zhang 张凤宇 ² ⁴, Zhibo Dang 党郅博 ¹, Xiao He 何霄 ¹, Chunxiong Luo 罗春雄 ² ⁴, Zhengchang Liu 刘正昌 ³, Pu Peng 彭璞 ¹, Yuchen Dai 戴宇琛 ³, Yijing Huang 黄逸婧 ¹, Yu Li 李瑜 ³, Zheyu Fang 方哲宇 ¹ ³
¹ School of Physics, Peking University, Beijing 100871, China
中国 北京 北京大学物理学院
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
中国 北京 北京大学 前沿交叉学科研究院 定量生物学中心 物理学院 人工微结构和介观物理国家重点实验室
³ Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
中国 北京 北京大学 前沿交叉学科研究院
⁴ Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China
中国 温州 中国科学院大学温州研究院
Opto-Electronic Science, 2023-02-09

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 of biomolecules based on reinforcement learning_1
Chiral detection of biomolecules based on reinforcement learning_2
Chiral detection of biomolecules based on reinforcement learning_3
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