Adsorption-Site-Guided Message Passing for Catalytic System Energy Prediction

Graph neural networks (GNNs) have been widely used for energy prediction in catalytic systems. However, existing methods primarily focus on modeling geometric relationships among atoms, while adsorption-site information has not been sufficiently incorporated into message passing. To address this limitation, we propose an adsorption-site-guided gated message-passing framework that encodes the distances between atoms and adsorption-site using radial basis functions (RBFs) and integrates the resulting site features into node or directed-edge representations. A gating mechanism then adaptively modulates the contribution of site information based on the current structural representations, enabling effective integration of adsorption-site information with the backbone representations. The proposed framework is integrated into SchNet, DimeNet++, and PaiNN and evaluated on the Initial Structure to Relaxed Energy (IS2RE) task of the Open Catalyst 2020(OC20) dataset. Experiments across three data regimes—10k, 100k, and the full training set—show that the proposed framework improves upon the corresponding baselines across most models and evaluation metrics, with more pronounced gains in the low-data regime. Ablation studies on SchNet further demonstrate that adsorption-site information and the gating mechanism play complementary roles, and their combination yields consistent performance improvements.These results demonstrate that the proposed framework can effectively exploit structural information associated with adsorption-site and is broadly applicable across different graph neural network architectures.