The frontier molecular orbitals of organic semiconductor materials play a crucial role in the performance of photoelectric devices, including organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), and organic photodetectors (OPDs). In this work, a model for predicting frontier molecular orbital of organic materials, including HOMO and LUMO levels, is established with the extreme gradient boosting algorithm and Klekota-Roth fingerprints. The correlation coefficients of HOMO or LUMO energy levels in the testing set are 0.75 and 0.84 in the transfer model from 11,626 DFT data in Harvard Energy database to 1198 experimental data in literature. The difference between the ML predicted value and the experimental value is smaller than the difference between ML prediction and DFT calculation, always less than 10%. Moreover, based on correlation and SHAP interpretability analysis, 13 key structural fragments influencing energy levels are selected to further verify the effective regulation of the frontier molecular orbital by the key structural fragments in practical applications. Considering the completely opposite regulatory functions of key structural fragments on HOMO and LUMO energy levels, four new Y6 derivatives, Y-PCP, Y-P6F, Y-PCF, and Y-P4FC, are designed to flexibly modify the HOMO and LUMO energy levels. The prediction trends of ML align closely with the computational trends from DFT. It is worth noting that the accuracy of LUMO energy level prediction by the prediction model makes up for the instability of DFT calculation on LUMO energy level. This work offers a cost-effective method to accelerate the acquisition of electronic properties of organic materials.

有机半导体材料的前沿分子轨道对有机光伏器件（OPV）、有机发光二极管（OLED）和有机光电探测器（OPD）等光电器件的性能起着至关重要的作用。在这项工作中，利用极端梯度增强算法和 Klekota-Roth 指纹建立了预测有机材料前沿分子轨道（包括 HOMO 和 LUMO 能级）的模型。从哈佛能源数据库中的11626条DFT数据到文献中的1198条实验数据的迁移模型中，测试集中HOMO或LUMO能级的相关系数分别为0.75和0.84。 ML预测值与实验值之间的差异小于ML预测与DFT计算之间的差异，始终小于10%。此外，基于相关性和SHAP可解释性分析，选取了13个影响能级的关键结构片段，进一步验证了关键结构片段在实际应用中对前沿分子轨道的有效调控。考虑到关键结构片段对HOMO和LUMO能级的完全相反的调节功能，设计了四种新的Y6衍生物Y-PCP、Y-P6F、Y-PCF和Y-P4FC，以灵活修改HOMO和LUMO能级。 ML 的预测趋势与 DFT 的计算趋势密切相关。值得注意的是，预测模型对LUMO能级预测的准确性弥补了LUMO能级DFT计算的不稳定性。这项工作提供了一种经济有效的方法来加速获得有机材料的电子特性。