In addition to predicting optical gaps, our training strategy, which combines quantum chemistry calculations and MFs, extends to predicting other fundamental properties of CPs such as HOMO and LUMO levels, which are also vital for their applications in electronics and solar cells. In our dataset of 1096 structures, HOMO levels were measured via cyclic voltammetry, while LUMO levels were derived from substituting optical gap values with HOMO values. Following the methodology detailed in the Methods section, we retrained the XGBoost model and presented the performance metrics in Table . Notably, XGBoost-2-H trained with DFT-calculated HOMO values of modified oligomers and ECFP6 exhibits higher accuracy in predicting experimentally measured HOMO values, achieving an R 2 of 0.5 and an MAE of 0.109 eV. Similarly, incorporating DFT-calculated LUMO levels enhances the accuracy of XGBoost-2 in predicting LUMO levels, with an R 2 of 0.6 and an MAE of 0.112 eV.