fig15

Advancing carbon dots research with machine learning: a comprehensive review

Figure 15. (A) Interpretability of CDs synthesis conditions enhanced using Pearson correlation coefficients and grey relational analysis. This figure is quoted with permission from Xu et al.[76], Copyright 2022, The Royal Society of Chemistry; (B) Contribution of CDs’ physicochemical properties to antibacterial performance quantified through Gini importance analysis. This figure is quoted with permission from Bian et al.[128]; (C) Dual role mechanisms of solvents systematically analyzed using tree models. This figure is quoted with permission from Hong et al.[75], Copyright 2022, American Chemical Society; (D) The QY mechanisms newly interpreted by integrating ML with molecular symmetry theory. This figure is quoted with permission from Chen et al.[130], Copyright 2024, Wiley-VCH GmbH. CDs: Carbon dots; QY: quantum yield; ML: machine learning.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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