We evaluate the network energetics with GAME-Net-UQ and run multiple microkinetic simulations at 473 K and 1 atm, as reported in the database. Details about the CRNs and MKM runs can be found in Supplementary Note 9. Comparing GAME-Net-UQ predictions to available DFT data on a subset of the studied metal surfaces, CARE shows an accuracy in terms of MAE of 0.27 eV for the reaction energy, and 0.55 eV for the activation energy (Fig. ). DFT studies typically focus on modeling reactive processes on the most stable surface facet. However, the observed catalytic activity in metal nanoparticles is attributed to the contributions from multiple exposed surfaces. The ability of GAME-Net-UQ to target multiple surfaces allows for predicting activity on different surfaces in a systematic way. Defining the overall reaction rate as a weighted sum of the rates on multiple surface facets leads to a general agreement with the experimental trend (Fig. ), while focusing on the most stable facet (e.g., (111) facet for fcc metals) would underestimate these predictions (Supplementary Fig. ).