(Peer-Reviewed) Artificial intelligence-assisted chiral nanophotonic designs
Xuanru Zhang 张璇如 ¹ ² ³, Tie Jun Cui 崔铁军 ¹ ² ³
¹ State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China
中国 南京 东南大学 毫米波国家重点实验室
² Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China
中国 南京 东南大学 电磁空间科学与技术研究院
³ School of Information Science and Engineering, Southeast University, Nanjing 210096, China
中国 南京 东南大学 信息科学与工程学院
Opto-Electronic Advances, 2023-10-31
Abstract
Chiral nanostructures can enhance the weak inherent chiral effects of biomolecules and highlight the important roles in chiral detection. However, the design of the chiral nanostructures is challenged by extensive theoretical simulations and explorative experiments.
Recently, Zheyu Fang’s group proposed a chiral nanostructure design method based on reinforcement learning, which can find out metallic chiral nanostructures with a sharp peak in circular dichroism spectra and enhance the chiral detection signals. This work envisions the powerful roles of artificial intelligence in nanophotonic designs.
Separation and identification of mixed signal for distributed acoustic sensor using deep learning
Huaxin Gu, Jingming Zhang, Xingwei Chen, Feihong Yu, Deyu Xu, Shuaiqi Liu, Weihao Lin, Xiaobing Shi, Zixing Huang, Xiongji Yang, Qingchang Hu, Liyang Shao
Opto-Electronic Advances
2025-11-25
