Unveiling the Hidden Energy Profiles of the Oxygen Evolution Reaction Via Machine Learning Analyses

The oxygen evolution reaction (OER)is a crucial electrochemicalprocess for hydrogen production in water electrolysis. However, dueto the involvement of multiple proton-coupled electron transfer steps,it is challenging to identify the specific elementary reaction thatlimits the rate of the OER. Here we employed a machine-learning-basedapproach to extract the reaction pathway exhaustively from experimentaldata. Genetic algorithms were applied to search for thermodynamicand kinetic parameters using the current-electrochemical potentialrelationship of the OER. Interestingly, analysis of the datasets revealedthe energy state distributions of reaction intermediates, which likelyoriginated in the interactions among intermediates or the distributionof multiple sites. Through our exhaustive analyses, we successfullyuncovered the hidden energy profiles of the OER. This approach canreveal the reaction pathway to activate for efficient hydrogen production,which facilitates the design of catalysts.