To further validate the ML models, we calculated the PL properties of NDI copolymers at highest PAM fractions for the external test set (Table and). Some of the new polymer structures lie significantly outside the applicability domain of the models, and the errors in the predictions tended to increase as the structure moved further away from the model domain. Extrapolation beyond the domain is increasingly risky, so the linear MLREM models are more likely to produce accurate predictions than the nonlinear BRANNLP models. Figure displays the predictive performance of the MLREM model for λem. Nearly all PAM have a linear correlation with an R 2 of 0.87, with the exception of FluoMA, which has a pentacyclic structure that is absent in the model training dataset, leading to unreliable predictions. The MLREM models predicted CIE values more accurately than for the BRANNLP models. The MLREM prediction errors for CIE x were 8-43% while those for CIE y were 2-77%, with NDI-polyAntMA, NDI-polyPyMA, and NDI-polyVPy having the largest CIE errors. The higher deviation between the predicted and experimental values is most likely because their bimodal emission spectra are not characteristics of the polymers used for model training. The relatively high prediction errors may also be due to this property spanning a relatively small dynamic range of ~0.2 to ~0.5, making it hard for ML models to discriminate between materials with similar CIE values. The MLREM-predicted CIE coordinates for NDI-coFluoMA, NDI-coVBp, NDI-co2VNaph and NDI-co1VNaph agree reasonably well with the experimental results, with an average error of less than 10% (Table ).