fig8

Figure 8. (A) After 103 iterations, the afterglow performance of CDs was improved using an active learning framework centered on the XGBoost model. The right panel shows the final search trajectory converging toward a high-lifetime region. These figures are quoted with permission from Yang et al.^[90]; (B) Full-color CDs successfully synthesized with only 63 experimental datasets through a multi-objective active learning optimization strategy. The right panel presents the reported utility-vs-iteration curve, indicating progressive improvement and convergence within 20 iterations. These figures are quoted with permission from Guo et al.^[91], Copyright 2024, American Chemical Society. CDs: Carbon dots; XGBoost: eXtreme gradient boosting; SVR: support vector regression; PLQY: photoluminescence quantum yield; MOO: multi-objective optimization; ML: machine learning.