Abstract
The design and optimization of organic materials with the specific functions for organic photovoltaic cells (OPV), organic light-emitting diodes (OLED), and organic photodetectors (OPD) with the customized performance are currently the time-consuming and costly process. Therefore, a molecular orbital energy level prediction platform for organic materials is established by utilizing the eXtreme Gradient Boosting (XGBT) algorithm and Klekota-Roth fingerprint (KRFP) in this study. And the prediction performance of prediction platform for predicting the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) of organic materials is characterized, which shows the accuracy is 99.0% and 97.5%, R is 0.88 and 0.93, RMSE is 0.077 and 0.126, MAE is 0.057 and 0.090, and MAPE is 0.01 and 0.025 in the training and test datasets, respectively. More importantly, thirteen key fragments are screened and their impact on HOMO and LUMO in organic materials is analyzed. Apparently, fluoromethane fragments can reduce HOMO and raise LUMO in organic materials, while Cycopropane fragments were observed to elevate HOMO and decrease LUMO. Based on the findings, Y6 molecules is modified to design four new Y6 derivatives, including Y6-DT, Y6-TF, Y6-TDF, and Y6-DFT for adjusting bandgap of organic materials. And the value difference of HOMO or LUMO in the new designed molecules between predicted by the platform and calculated by DFT is only below 5%. It is noteworthy that the platform prediction only costs an average time of 0.1 s. Moreover, this prediction platform also verifies the reported results in OLED and OPD-related literature, showing that the predicted accuracy is higher than 88.1%, the errors are limited to within 11.9%. All of these confirm the establishment of a cost-effective universal platform with high performance for accurately predicting and regulating the energy levels in organic materials.