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

Huazhang Guo, Yuhao Lu, Zhendong Lei, Hong Bao, Mingwan Zhang, Zeming Wang, Cuntai Guan (), Bijun Tang (), Zheng Liu () and Liang Wang ()
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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
Liang Wang: Shanghai University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49172-6

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