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

The oxygen evolution reaction (OER) is a crucial electrochemical process for hydrogen production in water electrolysis. However, due to the involvement of multiple proton-coupled electron transfer steps, it is challenging to identify the specific elementary reaction that limits the rate of the OER. Here we employed a machine-learning-based approach to extract the reaction pathway exhaustively from experimental data. Genetic algorithms were applied to search for thermodynamic and kinetic parameters using the current-electrochemical potential relationship of the OER. Interestingly, analysis of the datasets revealed the energy state distributions of reaction intermediates, which likely originated in the interactions among intermediates or the distribution of multiple sites. Through our exhaustive analyses, we successfully uncovered the hidden energy profiles of the OER. This approach can reveal the reaction pathway to activate for efficient hydrogen production, which facilitates the design of catalysts.