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Inverse design of chiral functional films by a robotic AI-guided system

Author

Listed:
  • Yifan Xie

    (University of Science and Technology of China)

  • Shuo Feng

    (University of Science and Technology of China)

  • Linxiao Deng

    (University of Science and Technology of China)

  • Aoran Cai

    (University of Science and Technology of China)

  • Liyu Gan

    (University of Science and Technology of China)

  • Zifan Jiang

    (University of Science and Technology of China)

  • Peng Yang

    (University of Science and Technology of China)

  • Guilin Ye

    (Hefei JiShu Quantum Technology Co. Ltd.)

  • Zaiqing Liu

    (University of Science and Technology of China)

  • Li Wen

    (University of Science and Technology of China)

  • Qing Zhu

    (University of Science and Technology of China)

  • Wanjun Zhang

    (Hefei JiShu Quantum Technology Co. Ltd.)

  • Zhanpeng Zhang

    (University of Science and Technology of China)

  • Jiahe Li

    (University of Science and Technology of China)

  • Zeyu Feng

    (University of Science and Technology of China)

  • Chutian Zhang

    (University of Science and Technology of China)

  • Wenjie Du

    (University of Science and Technology of China)

  • Lixin Xu

    (University of Science and Technology of China)

  • Jun Jiang

    (University of Science and Technology of China)

  • Xin Chen

    (Suzhou Laboratory)

  • Gang Zou

    (University of Science and Technology of China)

Abstract

Artificial chiral materials and nanostructures with strong and tuneable chiroptical activities, including sign, magnitude, and wavelength distribution, are useful owing to their potential applications in chiral sensing, enantioselective catalysis, and chiroptical devices. Thus, the inverse design and customized manufacturing of these materials is highly desirable. Here, we use an artificial intelligence (AI) guided robotic chemist to accurately predict chiroptical activities from the experimental absorption spectra and structure/process parameters, and generate chiral films with targeted chiroptical activities across the full visible spectrum. The robotic AI-chemist carries out the entire process, including chiral film construction, characterization, and testing. A machine learned reverse design model using spectrum embedded descriptors is developed to predict optimal structure/process parameters for any targeted chiroptical property. A series of chiral films with a dissymmetry factor as high as 1.9 (gabs ~ 1.9) are identified out of more than 100 million possible structures, and their feasible application in circular polarization-selective color filters for multiplex laser display and switchable circularly polarized (CP) luminescence is demonstrated. Our findings not only provide chiral films with the highest reported chiroptical activity, but also have great fundamental value for the inverse design of chiroptical materials.

Suggested Citation

  • Yifan Xie & Shuo Feng & Linxiao Deng & Aoran Cai & Liyu Gan & Zifan Jiang & Peng Yang & Guilin Ye & Zaiqing Liu & Li Wen & Qing Zhu & Wanjun Zhang & Zhanpeng Zhang & Jiahe Li & Zeyu Feng & Chutian Zha, 2023. "Inverse design of chiral functional films by a robotic AI-guided system," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41951-x
    DOI: 10.1038/s41467-023-41951-x
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