Generating machine learning models of material properties is of limited use unless their predictive power is assessed, and they are used to generate new insight into mechanisms or new materials with superior properties. As described above, the predictability of models was evaluated using a test set that was never used to train the models (Figure ). The ultimate test of model value is to synthesize and characterize materials predicted to be high performing by the models, that is, de novo prediction of the properties of novel polymers. Using the PL property ML models, new NDI polymers were designed, whose properties were predicted to be superior. To explore the full-color tuning possibility of NDI-based TSCT polymers, a series of donor-type monomers with extended π-conjugated structures, especially polycyclic aromatic groups, were selected. As shown in Figure (bottom), 8 monomers-4vinylbiphenyl (VBp), 9-fluorenyl methacrylate (FluoMA), 2-vinylnaphthalene (2VNaph), 1-vinylnaphtalene (1VNaph), 2-(methacryloyloxy)ethyl anthracene-9-carboxylate (AntMAc), 9-anthracenylmethyl methacrylate (AntMA), 1-pyrenemethyl methacrylate (PyMA), and 1-vinylpyrene (VPy)-were used, and their corresponding NDI-homopolymers were designed (Appendix B) as a de novo test set whose PL properties were predicted by the ML models.