Our data-driven energy estimator is fast and relatively cheap due to its small size and direct approach for predicting the DFT energy of relaxed structures, differently than machine learning potentials, which have two orders of magnitude more parameters and require multiple iterations to converge. The uncertainty quantification of the predictions provides an additional feature which can be propagated to reaction properties and microkinetic modeling, or exploited for active learning purposes and Bayesian optimization. The relatively low computational cost of our approach and the balance in the uncertainty quantification between intermediates and transition states removes the need for empirical rules like linear-scaling relationships, with GAME-Net-UQ covering a wider diversity in terms of metals, surfaces, and species, and being more accurate. The implemented interface of CARE for this model allows the energy estimation of thousands of intermediates/transition states in less than 1 CPU hour, representing a viable ML strategy for fast property prediction. Again, challenges in this area are linked to the introduction of lateral interactions (like solvents or high coverage), more detailed configurational analysis for adsorbates and expanding the structural diversity of metal surfaces. These aspects could be addressed by implementing lateral interactions via GNN , active learning and ∆-ML schemes . As for the energy estimator, this could be first-principles or data-driven, allowing the study of processes on catalysts different from the ones presented here such as alloys or metal oxides.