Property prediction of energetic materials based on directional-aware graph attention network

Energetic materials have widespread applications in military, aerospace, and other high-stakes domains. Accurate prediction of their explosive properties is critical for both material development and safe deployment. This paper proposes a Directional-Aware Graph Attention Network (DAGAN) model, which constructs node and edge representations incorporating fine-grained features such as atomic type distributions and chemical bond topological environments. A directional-aware graph attention architecture is designed and integrated with an adaptive training algorithm to enable deep mining of intrinsic molecular characteristics. Experimental results show that the DAGAN model, after hyper-parameter optimization, significantly outperforms traditional machine learning methods such as SVM, RF, and XGBoost in predicting explosive performance. Its attention mechanism effectively captures both local atomic interactions and global structural features, overcoming the limitations of incomplete information in conventional feature engineering. This work offers a novel perspective and method for the research and development of energetic materials.