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Machine learning-guided realization of full-color high-quantum-yield carbon quantum dots

Author

Listed:
  • Huazhang Guo

    (Shanghai University)

  • Yuhao Lu

    (Nanyang Technological University)

  • Zhendong Lei

    (Nanyang Technological University)

  • Hong Bao

    (Shanghai University)

  • Mingwan Zhang

    (Shanghai University)

  • Zeming Wang

    (Shanghai University)

  • Cuntai Guan

    (Nanyang Technological University)

  • Bijun Tang

    (Nanyang Technological University)

  • Zheng Liu

    (Nanyang Technological University
    UMI 3288, Research Techno Plaza
    National University of Singapore)

  • Liang Wang

    (Shanghai University
    Nanyang Technological University)

Abstract

Carbon quantum dots (CQDs) have versatile applications in luminescence, whereas identifying optimal synthesis conditions has been challenging due to numerous synthesis parameters and multiple desired outcomes, creating an enormous search space. In this study, we present a novel multi-objective optimization strategy utilizing a machine learning (ML) algorithm to intelligently guide the hydrothermal synthesis of CQDs. Our closed-loop approach learns from limited and sparse data, greatly reducing the research cycle and surpassing traditional trial-and-error methods. Moreover, it also reveals the intricate links between synthesis parameters and target properties and unifies the objective function to optimize multiple desired properties like full-color photoluminescence (PL) wavelength and high PL quantum yields (PLQY). With only 63 experiments, we achieve the synthesis of full-color fluorescent CQDs with high PLQY exceeding 60% across all colors. Our study represents a significant advancement in ML-guided CQDs synthesis, setting the stage for developing new materials with multiple desired properties.

Suggested Citation

  • Huazhang Guo & Yuhao Lu & Zhendong Lei & Hong Bao & Mingwan Zhang & Zeming Wang & Cuntai Guan & Bijun Tang & Zheng Liu & Liang Wang, 2024. "Machine learning-guided realization of full-color high-quantum-yield carbon quantum dots," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49172-6
    DOI: 10.1038/s41467-024-49172-6
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    1. Wenhui Gao & Jiuyang He & Lei Chen & Xiangqin Meng & Yana Ma & Liangliang Cheng & Kangsheng Tu & Xingfa Gao & Cui Liu & Mingzhen Zhang & Kelong Fan & Dai-Wen Pang & Xiyun Yan, 2023. "Deciphering the catalytic mechanism of superoxide dismutase activity of carbon dot nanozyme," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
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    3. Shuo Zhai & Heping Xie & Peng Cui & Daqin Guan & Jian Wang & Siyuan Zhao & Bin Chen & Yufei Song & Zongping Shao & Meng Ni, 2022. "A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells," Nature Energy, Nature, vol. 7(9), pages 866-875, September.
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