Chemical-intuitively, ΔE HL can qualitatively align with the photon emission energies of fluorescent molecules. Ye et al. reported a RF model combining the knowledge-based molecular representations from PaDEL and predicted the experimental emission wavelengths of 11k organic fluorescent molecules where MAE(testing) and R 2 (testing) were reported at 0.222 eV and 0.70, respectively (Table ) without taking into account any solvent description. Without the necessity of describing solvation effect for QM9 dataset, current G_Las05_RF model improves the predictivity in ΔE HL of QM9 molecules with MAE(testing) and R 2 (testing) at 0.177 eV and 0.96, respectively. This enhancement suggests that RF models can still provide reasonable predictivity and learning efficiency if the necessary information had been included in the descriptor space. However, the boundary of descriptor space needs to be pre-determined in according to mankind's domain knowledge. The importance of solvent descriptor for successfully predicting the experimental optical properties has been demonstrated by the recent machine learning and deep learning approaches.