BoTorch, an open-source framework built on PyTorch 61 was used to implement the multiobjective Bayesian optimization in Python. A FixedNoiseMultiTaskGP was used as the surrogate model to fit the observed data points. This model also permits feeding in experimentally determined noise around each data point (error bar). As a comparison, a MultiTaskGP model without noise input was also tested during initial benchmarking using a data set. A qNoisyExpectedHypervolumeImprovement (qNEHVI) acquisition function was used as a decision-making policy with a reference point of 550 mV and 10 mV for activity (ηOER) and stability (ΔηOER), respectively. The reference point represents a compromise between being realistically attainable and not too far from the desired optimum. The qNEHVI acquisition function allows batched optimization in which multiple candidates can be suggested per iteration, which is needed to couple MOBO guidance with the developed HT pipeline most efficiently. All features (i.e., compositions) were normalized to 1. The ηOER, Δη OER , and reference point had to be negated to depict a problem where higher values are more desired. This conversion is necessary, as the algorithm can only deal with maximization problems. Additionally, all values except the features were standardized using the StandardScaler from Scikit-learn, which removes the mean and scales the data to unit variance. During the initial benchmarking of MOBO using a data set, 5 randomly sampled compositions depict the starting condition. Then, one new composition is sampled from the data set during each iteration with the objective to improve the hypervolume. The optimization finishes when all candidates within the data set have been sampled. To gain statistical significance, 500 repetitions of such optimization runs have been performed. The seed for the random initialization went from 0 to 499. The FixedNoiseMultiTaskGP and MultiTaskGP models were compared against a random sampler with a random see concomitantly going from 0 to 499. The final performance was evaluated by calculating the average normalized hypervolume per iteration within an upper percentile of 75% and lower percentile of 25%. During MOBO-driven experiments, the acquisition function value is calculated for a constrained parameter space. The sum of all elements must be 1 (i.e., 100%), and each element must be within 0 and 1 (i.e., between 0 and 100%). The acquisition function then suggests 15 new candidates where each element is outputted with a value within the continuous 5-dimensional space (output value has many decimal places). Such elemental fractions are unfeasible to be synthesized using the pipetting robot. Thus, the most similar composition within the total search space of 1001 compositions (each differing in 10 at.%) is chosen based on the smallest Euclidean distance to the suggested candidate. Every evaluated candidate is eliminated from the total search space to avoid re-sampling.