The representation of the surface has been expanded to allow the study of structure sensitive processes. Surface effects are taken into account by including (i) species adsorbed on the 2 nd and 3 rd most stable surface facets of the metals in the training dataset (Supplementary Fig. ), (ii) the generalized coordination number of surface atoms as node feature and (iii) the 2-hop adsorbate nearest surface neighbors to distinguish hollow adsorption sites. As GAME-Net-UQ has been redefined as a mean-variance estimator , it returns DFT energy predictions as normal distributions E ∼ N (µ, σ 2 ) with the standard deviation σ representing the uncertainty (Fig. ). Details about training data, model architecture, UQ and training procedure can be found in Supplementary Notes 3-6.