Multi-objective optimization of chemical synthesisThere are different versions of BO algorithms depending on the acquisition function used to evaluate the surrogate model and the strategies used to suggest the most likely optimal values, for chemical reaction optimization the TSEMO (Thompson sampling efficient multi-objective optimization) algorithm has been the most widely used. Table summarizes the use of various ML algorithms for the optimization of chemical synthesis with multiple objectives function. The use of TSEMO for optimization of a chemical reaction was reported by Schweidtmann et al. . In this study the multi-objective Bayesian optimization (MOBO) was used to optimize a nucleophilic aromatic substitution (SNAr) reaction (Table , entry 1) and a N-benzylation reaction (Table , entry 2) using an automated flow reactor. The objectives of the optimization were to maximize the space-time yield (STY) of 22 and 24, while minimize either the E-factor for the SNAr reaction or the impurity concentration for the N-benzylation reaction. For both reactions there were four variables to optimize that included metrics for reaction time, reagent concentrations and temperature. After an initialization of 20 experimental conditions sampled by LHS, the choice of reaction conditions was left to the TSEMO algorithm to optimize the SNAr for a total of 48 iterations and the N-benzylation reaction for a total of 58 iterations. Both optimizations resulted in the discovery of a dense Pareto front with approximately 30-50% of the total suggested conditions resulting in non-dominated solutions.