In this study, we evaluated the effectiveness of three types of MFs for capturing the impact of alkyl side chains on the optical gaps of CPs: RDKit Descriptors, MACCS, and ECFP6 fingerprints, which were calculated from the SMILES strings of monomer structures containing alkyl side chains as detailed in the Methods section. Previous studies have shown these MFs are effective for training ML models to predict the photo-electronic properties of CP-based OSCs. The workflow for database preparation, feature engineering, model training, and transferability test is summarized in Figure . We trained six ML algorithms that were commonly used in materials sciences, including HGBR, LGBM, GBR, XGBoost, AdaBoost, and RF, using E DF T gap combined with different types of MFs as input parameters to predict E exp gap . Performance metrics, including R 2 , r, RMSE, and MAE, were detailed in Table -S11. Notably, the prediction accuracy of optical gap values is significantly enhanced, with the R 2 value increasing from 0.51 to as high as 0.77, by incorporating information from both the conjugated backbone (captured by E oligomer gap ) and the side chains (captured by MFs).