The task of optimization is prevalent in any scientific discipline. Material optimization and discovery, in particular, have propelled technological advancements, creating a materially different generation compared to previous ones. The current climate crisis poses a severe threat to the well-being of humanity, where innovation in energy materials is needed to tackle a monumental challenge of this scale. Establishing a renewable energy landscape is of utmost importance to secure a sustainable energy supply while maintaining current living standards in the near future. Electricity from wind and solar is intermittent, necessitating its storage into energy-dense molecules such as hydrogen to cover energy demands at any time, even during downtimes of renewable electricity production. Polymer electrolyte membrane water electrolysis (PEMWE) will be a cornerstone in the future energy transition, generating green hydrogen to end the reliance on current fossil-based energy carriers. Here, the oxygen evolution reaction (OER) is profoundly more sluggish than the hydrogen evolution reaction (HER). Given that anodic and cathodic reactions proceed simultaneously, the slower reaction ultimately determines the total device efficiency, making the improvement of OER kinetics a main target of many research endeavors in the water splitting community. Electrocatalysts build the heart of electrochemical devices and act as reaction promoters. So far, IrOx has become the state-of-the-art OER catalyst for PEMWEs. Despite decades of research, no marketable alternative consisting of cheap earth-abundant elements has emerged to replace this scarce and expensive noble metal catalyst, necessitating impactful materials innovation. Catalysts in commercial PEMWEs applications must not only be active but also durable over an extended time. The particular challenge lies in overcoming the inherent instability of non-noble metals at lower pHs, which is the operating environment of PEMWEs. Recently, some promising demonstrations have proved the viability of Co-and Mn-based oxides. For instance, Mondschein et al. showed that Co3O4 can perform OER over several days in strongly acidic electrolytes, albeit at low current densities, using a three-electrode cell. In terms of practical applicability, γ-MnO2 was used to operate a PEM setup for 400 h at 10 mA cm -2 . When 100 mA cm -2 was applied, the system shut down after 8 h, which was attributed to extensive Mn leaching and calls for the need to stabilize the noble metal-free OER catalyst further. One strategy to achieve higher stabilization is the expansion to multinary compositions. This method forms thermodynamically more stable alloys or bonds by adding elements that can alter electronic structures and act as stabilizing additives. The incorporation of Mn into the spinel lattice of Co3O4 can extend the catalyst lifetime by two orders of magnitude without compromising any activity during acidic OER. This effect was attributed to the formation of a stable Mn-O bond, suppressing dissolution. Chong et al. even demonstrated a La-and Mndoped cobalt spinel OER catalyst that was able to operate at current densities of 200 mA cm -2 within a PEM setup for 100 h. In fact, recent literature has shown that targeted alloying/doping of cobalt or manganese oxides with stabilizing elements such as Sb, Ti, or Sn can profoundly impact the overall catalyst stability, for instance through stabilization of a specific oxidation state or strengthening of the metal-oxygen bond. So far, studies on multimetallic transition metal catalysts for acidic OER have primarily focused on binary oxide systems. A plethora of unexplored mixed metal systems still exist that need to be screened for their viability as non-noble alternatives to IrOx. Constructing multinary systems represents a combinatorial problem, scaling exponentially with a new parameter (e.g., elements) added to the mix. Traditional material optimization is laborious and slow, partly due to the commonly utilized oneparameter-at-a-time approach, requiring many experiments to screen a grid of parameter combinations. Such grid search becomes time-intensive and thus infeasible when subjected to large search space. High-throughput (HT) methods have contributed greatly to this challenge, where individual laboratory tasks can be automated (e.g., synthesis, measurement, data analysis) to accelerate the testing of thousands of samples. Automation in HT methods not only expedites the workflow but introduces less human bias and error into the optimization campaign. Such approaches have already proven useful in screening non-noble multinary oxides toward acidic OER. The effectiveness of HT experiments in uncovering promising electrocatalysts lies in the proper definition of the main objective, i.e., which property to optimize. While many HT campaigns focus solely on the catalyst activity, screening the stability is crucial to adequately identify high-performing catalysts. However, the evaluation becomes more time and resource-intensive with each additional property being assessed. Combining thorough testing of each sample with a grid-search-based strategy for vast search spaces prolongs the total measurement time, losing the essence of HT screening. Additionally, not all experiments are equally useful. Spending time-intensive measurements on unpromising candidates during grid search can be a waste of resources. As an alternative, iterative approaches are more favorable, where a small subset across the entire parameter space is explored in the first iteration. The obtained information serves as prior knowledge, informing which regions in the parameter space to investigate next and which ones to neglect in order to optimize the objective. In a way, this heuristic approach has been performed by human scientists for many centuries. However, with the recent emergence of predictive machine learning (ML) algorithms, such experimental planning can be performed autonomously without human intervention, excluding human bias from research. ML techniques have catapulted the scientific community into a new paradigm, whereas the implementation for energy material research has just started. A ML algorithm is often discussed in conjunction with big data, with massive amounts of data providing the basis for training ML models. However, some ML approaches are specifically geared toward dealing with datapoor optimization campaigns, which are often prevalent in experimental material science, let alone electrocatalysis. One active learning approach is Bayesian optimization (BO), an adaptive sampling strategy relying on an iterative optimization process to find the global optimum in a predefined parameter space. The BO algorithm consists of the following steps: (i) initialization, in which some observations are collected, building the starting point for the optimization process. (ii) machine learning, in which a surrogate model (typically a Gaussian process) is fitted on the given observations. (iii) optimization policy, in which an acquisition function decides which parameter combinations are most informative to reach the global optimum. (iv) experimental evaluation of the newly suggested candidates and starting over at (ii). The feasibility of BO for material science has been demonstrated for photovoltaics, thin-films, 24 photocatalysts, or organic compounds, where it builds the heart of fully autonomous self-driving laboratories (SDL). SDLs are still very rarly applied to electrocatalytic applications. So far, only Black et al. have demonstrated a use-case in which they optimized a non-noble metal composition for acidic OER using an SDL. Besides SDLs, BO can be implemented into workflows without any robotic orchestration, such as computational studies to identify new electrocatalysts. BO has also been used to guide traditional laboratory workflows with manual involvement in either the synthesis or characterization to refine catalysts for electrochemical reactions. Yamauchi et al. have demonstrated the viability of BO for effectively screening the Pt-Pd-Au composition space for methanol oxidation. It was concluded that only 1% of the entire search space was screened to find the optimum. In another report, Arenz et al. used BO to explore a multidimensional high-entropy alloy composition space comprised of Pt-Ru-Pd-Rh-Au for H2/CO electrooxidation. Zelenay et al. implemented adaptive learning into an automated synthesis workflow for electrocatalyst development, which guided the optimization of a Fe-N-C catalyst for the oxygen reduction reaction. While these studies mostly focused on singly optimizing the catalyst activity, high-performing electrocatalysts must combine high activity and stability, as mentioned earlier, representing a multiobjective optimization task. Therefore, multiobjective BO (MOBO) is crucial, in which both objectives (activity and stability) are simultaneously optimized. In electrocatalysis, activity and stability tend to be inversely correlated, i.e., a catalyst with exceptionally high activity commonly shows poor stability and vice versa. Thus, MOBO aims to identify catalysts that possess the best compromise between these two properties. Our previous report demonstrated an HT workflow that allows rapid synthesis and testing of electrocatalysts for their OER activity and stability using single-task automation. However, the underlying optimization strategy always relied on a grid search without any feedback loop driven by ML. In this work, MOBO was implemented into a previously established HT platform to adaptively screen a Co-Mn-Sb-Sn-Ti oxide space to simultaneously maximize the activity and stability toward acidic OER. To test the viability of ML-driven experiments for the present optimization task, a data set was curated from a grid search of the Co-Mn-Sb-Sn-Ti oxide space. Based on this data set, a simulated MOBO was performed to evaluate whether the algorithm can identify the global optimum faster than a random sampler. After this validation, the algorithm was applied to suggest new compositions to be synthesized and screened in the laboratory to iteratively narrow down the optimal compositions. This strategy is illustrated in Figure . Once interesting compositions were identified, more detailed and time-consuming testing was carried out on selected candidates to study the observed behavior in greater depth, for example, by using operando mass spectrometry to probe the degradation behavior.