The previous results showed the validity of MOBO to optimize the OER catalyst activity and stability simultaneously in a 5dimensional space and laid the groundwork for the subsequent ML-driven experiments. The algorithm now suggests new compositions that a scientist would synthesize and screen in the laboratory, realizing a human-in-the-loop ML-driven catalyst optimization. The campaign begun with screening 15 homogeneously distributed samples over the entire quinary search space (each composition differing 50 at.%, see Table ). As a rule of thumb, BO campaigns should be initialized with around 2*(n+1) samples, where n is the dimensionality of the optimization campaign (5 in this case). The samples were screened using the same protocol shown in Figure . The observations serve as prior, whereupon the MOBO algorithm suggests 15 new candidates from a total composition space of 1001 candidates (each composition differing 10 at.%) that will be synthesized and screened. This loop is continued until a convergence is reached (see Figure1).