Downstream molecular models and properties of OCNet

The dataset for the optoelectronic properties in the gas phase is derived from the OCELOT chromophore dataset [1,2] created by Qianxiang Ai, Vinayak Bhat et al. , while the dataset for the optoelectronic properties in solution is sourced from Deep4Chem [3] created by Joonyoung F. Joung et al. We additionally provide the neural network weights finetuned by OCNet based on these datasets. [1] Bhat, V.; Sornberger, P.; Pokuri, B. S. S.; Duke, R.; Ganapathysubramanian, B.; Risko, C. Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches. Chemical Science 2023, 14, 203–213. [2] Ai, Q.; Bhat, V.; Ryno, S. M.; Jarolimek, K.; Sornberger, P.; Smith, A.; Haley, M. M.; Anthony, J. E.; Risko, C. OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductors. The Journal of Chemical Physics 2021, 154 . [3] Joung, J. F.; Han, M.; Jeong, M.; Park, S. Experimental database of optical properties of organic compounds. Scientific data 2020, 7, 295.本数据集的气相光电性质部分源自Qianxiang Ai、Vinayak Bhat等人构建的OCELOT发色团数据集（OCELOT chromophore dataset）[1,2]，而溶液相光电性质数据集则取自Joonyoung F. Joung等人创建的Deep4Chem数据集（Deep4Chem）[3]。 我们额外提供了基于上述数据集经OCNet微调得到的神经网络权重。 [1] Bhat, V.; Sornberger, P.; Pokuri, B. S. S.; Duke, R.; Ganapathysubramanian, B.; Risko, C. 基于分层机器学习方法预测有机π共轭分子的电子、氧化还原与光学性质. 《化学科学》, 2023, 14, 203–213. [2] Ai, Q.; Bhat, V.; Ryno, S. M.; Jarolimek, K.; Sornberger, P.; Smith, A.; Haley, M. M.; Anthony, J. E.; Risko, C. OCELOT：用于数据驱动研究以发现和设计结晶有机半导体的基础设施. 《化学物理学报》, 2021, 154. [3] Joung, J. F.; Han, M.; Jeong, M.; Park, S. 有机化合物光学性质实验数据库. 《科学数据》, 2020, 7, 295.