The graph dataset used to train and evaluate the model includes 12,608 entries, 80% of them are intermediates (2% in gas phase) and the remaining 20% are TSs representing bond-breaking events of species up to C 2 H 6 O 2 (Supplementary Fig. ). To distinguish the TS graphs from intermediates, a boolean feature is added to the graph edges to label the bond involved in the reaction, following a similar approach to the virtual bond described by Wang et al. (Fig. ). Consequently, the GNN architecture includes a topology-adaptive convolutional layer utilizing the encoded edge attribute. The GNN is trained with graphs representing the DFT-optimized geometries (Methods Section 4.1) and a streamlined version of DockOnSurf is employed as a pre-processing step to place adsorbates, replacing the need of DFT-optimized input geometries during inference. Adsorbate atoms with coordination lower than their valence are defined as anchoring points, while the surface adsorption sites are identified with ACAT .