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3
Figure 3. (A) UMAP for target space with narrow σ ; (B) The box plots of Fsp variations corresponding to different chemical spaces
emi
are shown for narrow σ ; (C) UMAP for target space with high Φ ; (D) UMAP for target space with high lg(ε max ). UMAP: Uniform
emi
QY
manifold approximation and projection.
Additionally, Union-GCN exhibits enhanced accuracy, with a mean absolute error (MAE) of 21.97 nm and
a root mean square error (RMSE) of 30.85 nm in predicting maximum emission wavelength in Figure 4A.
These normalized MAE represent only 2.7% of the total maximum emission wavelength range from 247 to
1,050 nm, demonstrating remarkable precision. With regard to other photophysical properties, the relatively
higher error observed in quantum yield prediction can primarily be attributed to the susceptibility of
quantum yield measurements to significant experimental errors and a weaker correlation with molecular
structure, which is evidenced by previous statistical analysis .
[26]

In the initial sampling space, the MolElite set comprised 4,766 molecules, representing 6.56% of the total,
while the MolMediocrity set accounted for only 0.775%. In the first iterative cycle, the proportion of
molecules in MolElite rose to 21.13%, whereas that of the MolMediocrity fell to 0.254%. The increased elite
proportion indicates that the enhanced sampling can continuously strengthen the learning process and
optimize the generation step of molecular generation. Throughout this process, the novelty, validity, and
uniqueness of the sampled molecules remained consistently high as shown in Figure 4B. Although the
proportion of high-quality molecules increased, the maintenance of novelty, efficacy, and uniqueness
demonstrates that the Molecular Generator can still generate a diverse range of molecular structures while
pursuing optimization. As the number of iterations increased, the proportion of molecules in MolElite
remained high, while the proportion of molecules classified as MolMediocrity showed a continuous decline.