We have demonstrated that ML models can leverage rationally designed E oligomer gap and MFs to improve the prediction accuracy of experimental optical gaps. Beyond accuracy, it is essential to validate the model's robustness and transferability with new datasets which have not been used in the ML model training process. In this study, we manually collected 227 newly synthesized CP structures from the literature, categorizing them into two groups based on their electron acceptor units. As shown in Figure , CP structures from group 1 contain at least one of the five acceptor units which were included in the training set; for example, BT and BTA units belong to A1 type (see Figure ). This subset of CP structures is applied to evaluate the interpolation performance of the trained ML models considering the close similarity of this subset of CP structures as compared to the training set. In contrast, group 2 contains five acceptor units which have not been seen in the training set (see Figure ), including Perylene Diimide (PDI), naphthalenediimide (NDI), dithieno[3',2':3,4;2",3":5,6]benzo[1,2-c][1,2,5]thiadiazole (DTBT), benzobisoxazole (BBX), and Y6. These acceptor units are important components of electron-accepting semiconductors for organic photovoltaic applications. For example, the Y6-based small molecule, first announced in 2019, achieved a record power conversion efficiency of 15.7% as an acceptor in