The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, which are key factors in optoelectronic devices, must be accurately estimated for newly designed materials. Here, we developed a deep learning (DL) model that was trained with an experimental database containing the HOMO and LUMO energies of 3026 organic molecules in solvents or solids and was capable of predicting the HOMO and LUMO energies of molecules with the mean absolute errors of 0.058 eV. Additionally, we demonstrated that our DL model was efficiently used to virtually screen optimal host and emitter molecules for organic light-emitting diodes (OLEDs). Deep-blue fluorescent OLEDs, which were fabricated with emitter and host molecules selected via DL prediction, exhibited narrow emission (bandwidth = 36 nm) at 412 nm and an external quantum efficiency of 6.58%. Our DL-assisted virtual screening method can be further applied to the development of component materials in optoelectronics.

对于新设计的材料，必须准确估计最高占据分子轨道 (HOMO) 和最低未占据分子轨道 (LUMO) 能量，它们是光电器件的关键因素。在这里，我们开发了一个深度学习 (DL) 模型，该模型使用包含溶剂或固体中 3026 个有机分子的 HOMO 和 LUMO 能量的实验数据库进行训练，能够预测分子的 HOMO 和 LUMO 能量，平均绝对误差为0.058 电子伏特。此外，我们证明了我们的 DL 模型可有效地用于虚拟筛选有机发光二极管 (OLED) 的最佳主体和发射体分子。深蓝色荧光 OLED，由通过 DL 预测选择的发射体和主体分子制成，在 412 nm 处表现出窄发射（带宽 = 36 nm），外量子效率为 6.58%。我们的 DL 辅助虚拟筛选方法可以进一步应用于光电子器件材料的开发。