Fused tricyclic organic compounds are an important class of organic electronic materials. In designing molecules for organic electronics, knowing what chemical structure that be used to tune the molecular property is one of the keys that can help to improve the material performance. In this research, we applied machine learning and data analytic approaches in addressing this problem. The energy states (Lowest Unoccupied Molecular Orbital (HOMO), Highest Occupied Molecular Orbitals (LUMO), singlet (E_s) and triplet (E_T) energy) of more than 10 thousand fused tricyclics are calculated. Corresponding descriptors are also generated. We find that the Coulomb matrix is a poorer descriptor than high-level descriptors in a multilayer perceptron neural network. Correlations as high as 0.95 is obtained using a multilayer perceptron neural network with Mean Absolute Error as low as 0.08 eV. The descriptors that are important in tuning the energy levels are revealed using the Random Forest algorithm. Correlations of such descriptors are also plotted. We found that the higher the number of tertiary amines, the deeper are the HOMO and LUMO levels. The presence of NN in the aromatic rings can be used to tune the E_S. However, there is no single dominant descriptor that can be correlated with the E_T. A collection of descriptors is found to give a far better correlation with E_T. This research demonstrated that machine learning and data analytics in guiding how certain chemical substructures correlate with the molecule energy states.

熔融三环有机化合物是一类重要的有机电子材料。在设计用于有机电子产品的分子时，了解用来调节分子特性的化学结构是有助于改善材料性能的关键之一。在这项研究中，我们应用了机器学习和数据分析方法来解决这个问题。能量态（最低未占据分子轨道（HOMO），最高占据分子轨道（LUMO），单重态（E _s）和三重态（E _T））的能量超过一万个稠合三环。相应的描述符也会生成。我们发现，在多层感知器神经网络中，库仑矩阵比高级描述符更差。使用多层感知器神经网络（平均绝对误差低至0.08 eV）可获得高达0.95的相关性。使用随机森林算法可以揭示在调节能量水平方面很重要的描述符。还绘制了此类描述符的相关性。我们发现，叔胺的数量越多，HOMO和LUMO的含量就越深。N的存在N的芳族环可以被用于调谐对E_小号。但是，没有单个显性描述符可以与E _T相关。发现描述符的集合与E _T具有更好的相关性。这项研究表明，机器学习和数据分析可以指导某些化学亚结构与分子能态之间的关系。