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Density functional theory calculations can be used to identify dominantreaction mechanisms. However, the dominant reaction mechanism is sensitive tochoice of the exchange correlation functional. Here, we demonstrate using theexample case of chlorine evolution reaction on rutile oxides, which can occurthrough at least three reaction mechanisms each mediated by different surfaceintermediates and active sites. We utilize Bayesian error estimationcapabilities within the BEEF-vdW exchange correlation (XC) functional toquantify the uncertainty associated with predictions of the operative reactionmechanism by systematically propagating the uncertainty originating fromDFT-computed adsorption free energies. We construct surface Pourbaix diagramsbased on the calculated adsorption free energies for rutile oxides of Ru, Ir,Ti, Pt, V, Sn and Rh. We utilize confidence-value (c-value) to determine thedegree of confidence in the predicted surface phase diagrams. Using the scalingrelations between the adsorption energies of intermediates we construct ageneralized Pourbaix diagram showing the stable surface composition as afunction of potential and the oxygen binding energy on the cus site. This isused to consistently determine activity volcano relationships. We incorporatethe uncertainty in linear scaling relations to quantify the confidence ingeneralized Pourbaix diagram and the associated activity. This allows us tocompute the expectation limiting potential as a function of $\Delta E_{O^c}$,which provides a more appropriate activity measure incorporating DFTuncertainty. We show that the confidence in the classification problem ofidentifying the active reaction mechanism is much better than the predictionproblem of determining catalytic activity.

Quantifying Confidence in DFT Predicted Surface Pourbaix Diagrams and Associated Reaction Pathways for Chlorine Evolution