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This work highlights the potential of earth-abundant mixed-metal oxidecatalysts for the acid-based oxygen evolution reaction. These catalysts offernumerous combinations of metal-centre compositions, which can enhance catalyticactivity and stability compared to precious-metal-based catalysts commonly usedtoday. Despite substantial research in this field, there is a need for newmethods and approaches to accelerate the exploration of these materials. Inthis study, we present a comprehensive approach to designing, developing, andimplementing a self-driving laboratory to optimize the electrodepositionsynthesis of amorphous mixed-metal oxide catalysts for the acidic oxygenevolution reaction. We particularly emphasize the development of methodologiesto address experimental variability. We investigate crucial parameters andconsiderations when transitioning from manual bench-top synthesis andevaluation to automation and machine learning guided optimization. We addressboth experimental and optimization algorithm considerations in the presence ofexperimental variability. To illustrate our approach, we demonstrate theoptimization of CoFeMnPbOx electrodeposited catalyst materials through multiplecampaigns. Our results highlight considerations for optimizing overpotentialand stability based on the outcomes of our experiments.

A Critical Evaluation of a Self-Driving Laboratory for the Optimization  of Electrodeposited Earth-Abundant Mixed-Metal Oxide Catalysts for the Oxygen  Evolution Reaction (OER)