To comprehensively demonstrate the performance of OCNet in different downstream scenarios, we evaluate its accuracy on QM-calculated or experiment-measured optoelectronic properties and transfer integrals in crystal or film environments. For all property prediction tasks, we set the training-to-test ratio at 8:2 and used mean absolute error (MAE) and the coefficient of determination (R 2 ) to evaluate model accuracy. Moreover, to make the OCNet more accessible to the broader research community, we also integrated these downstream property prediction models into a user-friendly interactive web app() for material design in various scenarios(Figure ), such as photovoltaic and display.