We utilize the open-source OCELOT chromophore dataset, which provides DFT and TDDFT-computed data, to evaluate the accuracy of OCNet in predicting QM-calculated optoelectronic properties. Specifically, we compare the performance of OCNet with reported SOTA models in terms of the HOMO-LUMO gap (H-L), lowest-lying singlet excitation energies (S0-S1), electron reorganization energies (ER), and hole reorganization energies (HR) (Figure ). We define the accuracy score of OCNet on the dataset as the ratio of the MAE of the SOTA model to the MAE of OCNet. It can be observed that OCNet achieves the highest accuracy across all four property prediction tasks, with accuracy scores improving by at least 15% for all tasks and by 60% for the HR prediction task. We also demonstrate the correlation between the OCNet predictions and the corresponding QM-calculated values for all four properties(Figure ). OCNet successfully achieves quantitative predictions for the S0-S1 and H-L tasks, with MAEs of 0.199 eV and 0.008 eV, and R 2 values of 0.803 and 0.987, respectively. OCNet achieves MAEs of 0.082 eV and 0.087 eV for the ER and HR prediction tasks, with R 2 values of 0.575 and 0.511, respectively. This level of accuracy is sufficient for screening molecules with low reorganization energies. We further compare the MAE and R 2 of the reported SOTA model, as well as OCNet with(w/) and without(w/o) pre-training, across all four properties (Table and Subsequently, we evaluate the accuracy of OCNet on the experimental optoelectronic dataset, Deep4Chem. Given the complexity of the solution environment, reported SOTA models typically employ strategies such as directed message passing, introducing additional edge and subgraph information or integrating domain prior knowledge to enhance molecular representation. We integrate domain-specific features utilized in SuboptGraph into our molecular representation to predict absorption wavelength (Abs.), emission wavelength (Emi.), photoluminescence quantum yield (PLQY), and full width at half maximum (FWHM). We compare the performance of OCNet with SOTA models in predicting these four properties (Figure ). The accuracy score of OCNet on the datasets is defined as the ratio of the MAE of the SOTA model to the MAE of OCNet. Although SOTA 2D graph convolutional models incorporate additional edge and subgraph information to enhance molecular representation, our 3D geometry-based OCNet achieves improvements of 18% and 13% in accuracy score for the prediction of absorption and emission wavelengths, respectively. Since our pre-training does not include tasks related to PLQY or FWHM, the performance improvement of OCNet for these properties is relatively modest, approximately 5%. We also demonstrate the correlation between the OCNet predicted values and experimental values(Figure ). OCNet accurately predicts absorption and emission wavelengths, achieving MAEs of 7.085 nm and 11.167 nm, respectively, with corresponding R 2 values of 0.982 and 0.949. The accuracy of OCNet in predicting PLQY and FWHM is also capable of screening molecules with high PLQY and narrow emission, with MAEs of 0.101 and 9.123 nm, and R 2 values of 0.722 and 0.719. We further compare the MAEs and R 2 values of the reported models (Uni-Mol and reported SOTA model SuboptGraph), along with OCNet w/ and w/o pre-training, for absorption and emission wavelength predictions (Figure , Tables and). Due to significant structural differences between drug molecules and conjugated molecules, Uni-Mol, which is pre-trained on drug molecule conformations, exhibits relatively poor accuracy in absorption and emission wavelength prediction tasks. Specifically, the MAE for emission energy prediction reaches 16 nm, whereas the MAE for the other models remains below 13 nm. Furthermore, both OCNet w/ and w/o domain features achieve lower MAEs compared to the reported SOTA model, demonstrating the effectiveness of our molecular representations pre-trained on conjugated systems. In addition, OCNet w/ domain features exhibits slightly higher accuracy in terms of both MAE and R 2 metrics. This indicates that integration with domain knowledge can enhance the representational capacity of OCNet, further relieving the data scarcity in materials science.