Machine learning (ML) models are undoubtedly paving a way toward intelligent chemistry systems, establishing themselves as a primary research focus in process systems engineering. Among them, Bayesian optimization (BO) — a self-optimization method that expedites the screening of experimental parameters — has particularly excelled in practical applications.
The intersection of ML and chemical engineering presents exciting opportunities for researchers across diverse disciplines. Chemists, the primary end-users, prioritize the convenience and specialization of these algorithms. Thus, it is desirable to package the algorithm as a user-friendly toolbox that is accessible to all users, regardless of their familiarity with the underlying principles and details. Moreover, there is a growing emphasis on integrating more domain knowledge to enhance applicability, rather than using generic statistical ML models. Historically, a lack of familiarity with chemistry has prohibited ML practitioners from integrating this domain knowledge. In the March AIChE Journal article, “CAPBO: A Cost-Aware Parallelized Bayesian Optimization Method for Chemical Reaction Optimization,” Zhihong Yuan (Tsinghua Univ.)...
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