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

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 Zhang, Wenjie Du, Lixin Xu, Jun Jiang (), Xin Chen () and Gang Zou ()
Additional contact information
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

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-41951-x

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