UQ can be exploited within an active learning strategy to refine with DFT the intermediates with the highest number of participated elementary reactions, the so-called 'hubs' , and to propagate the uncertainty to microkinetic simulations. The energy estimator interface in CARE can be connected to databases to avoid the estimation of the energy intermediates already evaluated at the DFT level. While this strategy leads to minimal computational savings, the actual gain is a reduced uncertainty and improved accuracy for the reactions involving the intermediates evaluated with DFT. As an example, employing the DFT energy of the adsorbed H * , the main hub in the C 1 O 2 CRN, positively affects the evaluation of 48% of the reactions in the network by reducing their error and associated uncertainty.