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

Guo, Huazhang; Lu, Yuhao; Lei, Zhendong; Bao, Hong; Zhang, Mingwan; Wang, Zeming; Guan, Cuntai; Tang, Bijun; Liu, Zheng; Wang, Liang

doi:10.1038/s41467-024-49172-6

articleNature CommunicationsJun 6, 2024GOLD OA

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

HGHuazhang Guo YLYuhao Lu ZLZhendong Lei HBHong Bao MZMingwan Zhang

Shanghai University · Nanyang Technological University · +1 more institution

PubMed

Indexed incrossrefdoajpubmed

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…

Citation impact

140

total citations

FWCI: 15.19
Percentile: 100%
References: 63

Citations per year

Authors

10

Topics & keywords

Topics

Keywords

Quantum yield
Computer science
Photoluminescence
Quantum dot
Carbon quantum dots
Quantum
Realization (probability)
Fluorescence

No related works found for this paper.