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Autonomous process optimization involves the human-intervention-free exploration of a range of predefined process parameters to improve responses such as reaction yield, product selectivity, and catalyst turnover number. The definition of a set of meaningful, broad, and unbiased process parameters is arguably the most critical aspect of a successful optimization. Work to date has focused on the multivariate optimization of continuous parameters such as temperature, stoichiometry, and time; however, vital categorical parameters such as reagent, solvent, or catalyst have rarely been incorporated. In fact, leading examples involving continuous and categorical parameter combinations have been limited to fewer than eight catalysts or ten solvents24,25,26. Furthermore, in these examples, categorical parameter selection was guided through chemical intuition, potentially introducing an element of bias into the experimental design. Thus, in our view, categorical parameter selection in the context of autonomous process optimization remains an unsolved challenge. We envisioned developing a more systematic method for the selection of a broad and diverse set of categorical parameters to fully represent the chemical space, driving more effective optimization campaigns.
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