Guo Huazhang, Lu Yuhao, Lei Zhendong, Bao Hong, Zhang Mingwan, Wang Zeming, Guan Cuntai, Tang Bijun, Liu Zheng, Wang Liang
Institute of Nanochemistry and Nanobiology, School of Environmental and Chemical Engineering, Shanghai University, 99 Shangda Road, BaoShan District, Shanghai, 200444, China.
College of Computing and Data Science, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
Nat Commun. 2024 Jun 6;15(1):4843. doi: 10.1038/s41467-024-49172-6.
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.
碳量子点（CQDs）在发光领域有广泛应用，然而由于众多合成参数和多种期望结果，确定最佳合成条件颇具挑战，这创造了一个巨大的搜索空间。在本研究中，我们提出一种新颖的多目标优化策略，利用机器学习（ML）算法智能指导CQDs的水热合成。我们的闭环方法从有限且稀疏的数据中学习，大大缩短了研究周期，超越了传统的试错方法。此外，它还揭示了合成参数与目标性质之间的复杂联系，并统一目标函数以优化多种期望性质，如全色光致发光（PL）波长和高光致发光量子产率（PLQY）。仅通过63次实验，我们就实现了全色荧光CQDs的合成，所有颜色的PLQY均超过60%。我们的研究代表了ML指导CQDs合成的重大进展，并为开发具有多种期望性质的新材料奠定了基础。
