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

TL;DR

This paper uses Bayesian error estimation within DFT to quantify uncertainty in surface Pourbaix diagrams and reaction mechanisms for chlorine evolution on rutile oxides, improving confidence in predictions of active surface phases and activity.

Contribution

It introduces a method to quantify DFT prediction uncertainty for surface phase diagrams and reaction mechanisms using Bayesian error estimation, enhancing reliability of catalytic activity predictions.

Findings

Uncertainty quantification improves confidence in surface phase diagrams.

Bayesian error estimation helps identify dominant reaction mechanisms.

Generalized Pourbaix diagrams incorporate DFT uncertainty for better activity prediction.

Abstract

Density functional theory calculations can be used to identify dominant reaction mechanisms. However, the dominant reaction mechanism is sensitive to choice of the exchange correlation functional. Here, we demonstrate using the example case of chlorine evolution reaction on rutile oxides, which can occur through at least three reaction mechanisms each mediated by different surface intermediates and active sites. We utilize Bayesian error estimation capabilities within the BEEF-vdW exchange correlation (XC) functional to quantify the uncertainty associated with predictions of the operative reaction mechanism by systematically propagating the uncertainty originating from DFT-computed adsorption free energies. We construct surface Pourbaix diagrams based 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 the degree of confidence in the predicted surface phase diagrams. Using the scaling relations between the adsorption energies of intermediates we construct a generalized Pourbaix diagram showing the stable surface composition as a function of potential and the oxygen binding energy on the cus site. This is used to consistently determine activity volcano relationships. We incorporate the uncertainty in linear scaling relations to quantify the confidence in generalized Pourbaix diagram and the associated activity. This allows us to compute the expectation limiting potential as a function of $\Delta E_{O^c}$, which provides a more appropriate activity measure incorporating DFT uncertainty. We show that the confidence in the classification problem of identifying the active reaction mechanism is much better than the prediction problem of determining catalytic activity.