Maximizing Reaction Yield Through Bayesian Optimization
Bayesian Optimization provides a more efficient alternative to traditional Design of Experiments (DoE) for process development, particularly when navigating multiple factors and levels. This case study highlights an innovative approach involving the exploration of areas characterized by high uncertainty and high potential yield. This focus enabled the identification of optimal operating ranges for critical factors, ultimately maximizing reaction yield.