Work Function Prediction by Graph Neural Networks for Configurationally Hybridized Boron-Doped Graphene

Graphene, serving as electrodes, is widely applied in electronic and optoelectronic devices. Work function as one of the fundamental intrinsic characteristics of graphene directly affects the interfacial properties of the electrodes, thereby affecting the performance of the devices. Much work has been done to regulate the work function of graphene to expand its application fields, and doping has been demonstrated as an effective method. However, the numerous types of doped graphene make the investigation of its work function time-consuming and labor-intensive. In order to quickly obtain the relationship between the structure and property, a deep learning method is employed to predict the work function in this study. Specifically, a data set of over 30,000 compositions with the work function on boron-doped graphene at different concentrations and doping positions via density functional theory simulations was established through ab initio calculations. Then, a novel fusion model (GT-Net) combining transformers and graph neural networks (GNNs) was proposed. After that, improved effective GNN-based descriptors were developed. Finally, three different GNN methods were compared, and the results show that the proposed method could accurately predicate the work function with the R² = 0.975 and RMSE = 0.027. This study not only provides the possibility of designing materials with specific properties at the atomic level but also demonstrates the performance of GNNs on graph-level tasks with the same graph structure and atomic number.