Optimizing telescoped heterogeneous catalysis with noise-resilient multi-objective Bayesian optimization

This study evaluates the noise resilience of multi-objective Bayesian optimization (MOBO) algorithms in chemical synthesis, an aspect critical for processes like telescoped reactions and heterogeneous catalysis but seldom systematically assessed. Through simulation experiments on amidation, acylation, and SNAr reactions under varying noise levels, we identify the qNEHVI acquisition function as notably proficient in handling noise. Subsequently, qNEHVI is employed to optimize a two-step heterogeneous catalysis for the continuous-flow synthesis of hexafluoroisopropanol. Remarkable optimization is achieved within just 29 experimental runs, resulting in an E-factor of 0.125 and a yield of 93.1%. The optimal conditions are established at 5.0 sccm and 120 degrees C for the first step, and 94.0 sccm and 170 degrees C for the second step. This research highlights qNEHVI's potential in noisy multi-objective optimization and its practical utility in refining complex synthesis processes.