To ensure generality, we only use the system's elements and distance kernel matrix as the initial atomic and pair representations. These representations are then aggregated into a global molecular representation through 15 encoder layers using the attention mechanism (detailed in Figure and the Methods section). In this work, we choose a SE(3) Transformer architecture which contains 15 encoder layers to construct molecular representation learning (MRL) models, as it has demonstrated high performance on small organic and drug molecules. Then, we optimize all the weight parameters of the molecular representation learning (MRL) models through pre-training on our molecular and bimolecular databases(Figure ). By learning from large amounts of unlabeled structures and TB-level optoelectronic properties and transfer integral data, we can significantly reduce the data requirements of our molecular and bimolecular representation when fine-tuning downstream optoelectronic and transport-related tasks.