E-mail Alert
RSS
| Citation: |
Zhang XR, Cui TJ. Artificial intelligence-assisted chiral nanophotonic designs. Opto-Electron Adv 6, 230057 (2023). doi: 10.29026/oea.2023.230057
|
News & Views Open Access
Artificial intelligence-assisted chiral nanophotonic designs
-
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. -
-
References
[1] Barron LD. Molecular Light Scattering and Optical Activity 2nd ed (Cambridge University Press, Cambridge, 2009). [2] Droulias S, Bougas L. Chiral sensing with achiral anisotropic metasurfaces. Phys Rev B 104, 075412 (2021). doi: 10.1103/PhysRevB.104.075412 [3] Zhao Y, Askarpour AN, Sun LY, Shi JW, Li XQ et al. Chirality detection of enantiomers using twisted optical metamaterials. Nat Commun 8, 14180 (2017). doi: 10.1038/ncomms14180 [4] Brullot W, Vanbel MK, Swusten T, Verbiest T. Resolving enantiomers using the optical angular momentum of twisted light. Sci Adv 2, e1501349 (2016). doi: 10.1126/sciadv.1501349 [5] Zhang XR, Cui TJ. Single-particle dichroism using orbital angular momentum in a microwave plasmonic resonator. ACS Photonics 7, 3291–3297 (2020). doi: 10.1021/acsphotonics.0c01139 [6] Zhang XR, Zhao X, Cui TJ. Microwave vortex transceiver system with continuous tunability using identical plasmonic resonators. Adv Opt Mater 10, 2201543 (2022). doi: 10.1002/adom.202201543 [7] Zhang Q, Liu C, Wan X, Zhang L, Liu S et al. Machine-learning designs of anisotropic digital coding metasurfaces. Adv Theory Simul 2, 1800132 (2019). doi: 10.1002/adts.201800132 [8] Jiang JQ, Fan JA. Global optimization of dielectric metasurfaces using a physics-driven neural network. Nano Lett 19, 5366–5372 (2019). doi: 10.1021/acs.nanolett.9b01857 [9] Melati D, Grinberg Y, Dezfouli MK, Janz S, Cheben P et al. Mapping the global design space of nanophotonic components using machine learning pattern recognition. Nat Commun 10, 4775 (2019). doi: 10.1038/s41467-019-12698-1 [10] Khatib O, Ren SM, Malof J, Padilla WJ. Deep learning the electromagnetic properties of metamaterials—a comprehensive review. Adv Funct Mater 31, 2101748 (2021). doi: 10.1002/adfm.202101748 [11] Liu ZH, Liu XH, Xiao ZY, Lu CC, Wang HQ et al. Integrated nanophotonic wavelength router based on an intelligent algorithm. Optica 6, 1367–1373 (2019). doi: 10.1364/OPTICA.6.001367 [12] Liu C, Yu WM, Ma Q, Li LL, Cui TJ. Intelligent coding metasurface holograms by physics-assisted unsupervised generative adversarial network. Photonics Res 9, B159–B167 (2021). doi: 10.1364/PRJ.416287 [13] Chen YX, Zhang FY, Dang ZB, He X, Luo CX et al. Chiral detection of biomolecules based on reinforcement learning. Opto-Electron Sci 2, 220019 (2023). doi: 10.29026/oes.2023.220019 -
Access History
Figures(1)
Article Metrics
Export File
Citation
Zhang XR, Cui TJ. Artificial intelligence-assisted chiral nanophotonic designs. Opto-Electron Adv 6, 230057 (2023). doi: 10.29026/oea.2023.230057
Format
Content
DownLoad:
-
Figure 1.
The design workflow of the chiral nanostructures based on reinforcement learning. Figure reproduced with permission from ref.13, under a Creative Commons Attribution 4.0 International License.
