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.