Attempts from various levels of theory have been made to address this question. On the one hand, at the molecular level, traditional quantum chemistry methods can delineate the electronic relaxation processes among different electronic states based on the geometric configuration of a single molecule, enabling relatively accurate predictions on the material's optoelectronic properties. The feasibility of such method actually lies in the fact that the intermolecular interaction between organic molecules is weak. 12 By leveraging the geometric configuration of bimolecules in films, the electronic coupling (transfer integral) between molecules can be described to assess the electron transport probability. However, the aforementioned simulation methods entail high computational demands as well as master specialized computational techniques of the researcher, such as the selection of functionals and basis sets and the preparation of force field topology files. On the other hand, data-driven methods have exhibited significant potential in predicting computational or experimentally characterized properties with high accuracy and efficiency. Yet, these methods usually rely on feature engineering predicated on expert domain knowledge, often suffering from poor transferability to dissimilar property tasks and being at a disadvantage when dealing with high-diversity databases.