When a specific target compound is determined for process development towards manufacturing, reaction condition optimization is necessary to improve the synthesis efficiency along with other considerations (e.g., costs and impurity generation) . Instead of traditional manual one-factor-at-time (OFAT) optimization, the recent development of optimization algorithms, such as Bayesian optimization (BO) , Nelder-Mead Simplex , stable noisy optimization by branch and fit (SNOBFIT) algorithm , and the mixed-integer nonlinear program (MINLP) algorithm , have enabled the automated experimental platforms to perform closed-loop reaction optimization in an autonomous manner. However, akin to the HTS technology mentioned previously, the steep learning curve associated with mastering automated hardware and optimization algorithms prevents the widespread adoption of the self-driven reaction optimization workflow as a routine tool in process development, despite its demonstrated effectiveness.