RAKAN: An Efficient Residual-Connected Graph Neural Network for Predicting Molecular Potential Energy Based on Attention Mechanism and Kolmogorov-Arnold

Molecular potential energy prediction is fundamental to molecular dynamics simulations. Recent advances in geometric deep learning have yielded numerous models that balance speed and accuracy. However, existing approaches often struggle with limited feature extraction and suboptimal function fitting. Enhancing prediction accuracy remains a critical challenge. In this study, we introduce RAKAN, a novel residual-connected graph neural network that leverages attention mechanisms and Kolmogorov-Arnold representation to improve feature extraction and prediction precision. RAKAN surpasses the existing state-of-the-art approaches on the MD17 benchmark dataset and demonstrates superior performance in predicting chemical properties on the QM9 dataset. These results demonstrate RAKAN’s superior accuracy and robust feature learning capabilities, advancing the field of molecular potential energy prediction.