For both group 1 and group 2 CP structures, we constructed the modified oligomers using the two-step procedure (see Figure ) to obtain the E oligomer gap values and converted the SMILES strings of the alkyl-side-chain-containing monomers into ECFP6 features. Then, we applied the XGBoost models previously trained with the 1096 dataset to predict the E exp gap of both groups without further retraining. The performance metrics are presented in Table . XGBoost-2, trained with E oligomer gap and ECFP6, accurately predicted the optical gaps of group 1 CPs with most MAEs below 0.1 eV, demonstrating excellent interpolation performance. Interestingly, XGBoost-1, trained with only ECFP6, also showed superior interpolation performance, resulting in lower RMSE and MAE than XGBoost-2 across all CP types in group 1. These results suggest that ECFP6 effectively captures the electronic properties of similar CPs within the same chemical design space. In fact, ECFP6 has been widely used to measure the similarities of various organic molecules in previous studies. We then assessed the extrapolation performance of both models with group 2 CPs. As shown in Table , XGBoost-2 significantly outperformed XGBoost-1, yielding substantially lower RMSE and MAE values across all CP types. For example, the MAE for Y6 based CPs decreases from 0.418 eV to 0.211 eV with XGBoost-2. Previous studies have also shown that conventional ML models trained with molecular descriptors may perform well in the chemical structure space similar to the training set, whereas the extrapolation to the new structure space is challenging due to the lack of physical/chemical insight from the input descriptors. Our results demonstrate that the XGBoost-2 model, trained with both E oligomer gap and ECFP6, excels in both high interpolation and extrapolation performance. This superior transferability is originated from the excellent robustness of DFT methods and rationally designed oligomer structures, effectively capturing the electronic properties of the CPs. Indeed, as shown in Figure oligomer gap are highly correlated with E exp gap for both group 1 and group 2 CPs. , XGBoost-2-plus achieved enhanced prediction accuracy, with a lower RMSE of 0.241 eV and MAE of 0.213 eV compared to XGBoost-2 (0.333 eV and 0.3 eV, respectively). Particularly, the prediction errors of the XGBoost-2-plus were significantly reduced for each polymer in the test set, demonstrating the effectiveness of data augmentation in improving model performance. It is important to note that experimentally measured optical gap values for CPs can vary across different labs and experiments, introducing potential inconsistencies and errors. Factors such as processing conditions, solvents, additives, and film morphology can influence these measurements. Utilizing larger and more accurate experimental datasets can enhance the predictive accuracy of ML models for optical gaps.