  This work highlights the potential of earth-abundant mixed-metal oxide
catalysts for the acid-based oxygen evolution reaction. These catalysts offer
numerous combinations of metal-centre compositions, which can enhance catalytic
activity and stability compared to precious-metal-based catalysts commonly used
today. Despite substantial research in this field, there is a need for new
methods and approaches to accelerate the exploration of these materials. In
this study, we present a comprehensive approach to designing, developing, and
implementing a self-driving laboratory to optimize the electrodeposition
synthesis of amorphous mixed-metal oxide catalysts for the acidic oxygen
evolution reaction. We particularly emphasize the development of methodologies
to address experimental variability. We investigate crucial parameters and
considerations when transitioning from manual bench-top synthesis and
evaluation to automation and machine learning guided optimization. We address
both experimental and optimization algorithm considerations in the presence of
experimental variability. To illustrate our approach, we demonstrate the
optimization of CoFeMnPbOx electrodeposited catalyst materials through multiple
campaigns. Our results highlight considerations for optimizing overpotential
and stability based on the outcomes of our experiments.
