CAPBO: A cost‐aware parallelized Bayesian optimization method for chemical reaction optimization

Abstract

Bayesian optimization employs probabilistic surrogate models to effectively address expensive and time‐consuming closed‐loop chemical experimental design. However, traditional Bayesian optimization focuses on reducing the number of iterations and follows an inherently sequential process (with one new data point sampled in each iteration), which is an inefficient means of exploiting and characterizing reactions using parallel microreactors. In this article, we present an approach that overcomes this issue by considering two essential factors: experimental cost sensitivity and parallelization. We propose a novel cost‐aware parallelized Bayesian optimization method (CAPBO) in which the goal is changed from reducing the number of iterations to reducing the experimental cost, with quantitative experimental speed‐up achieved through parallelization. The combination of these two items greatly fits the need of reaction optimization, leading to better optimization effects. Benchmarking and case studies demonstrate that the proposed method significantly outperforms traditional Bayesian optimization and considerably improves the optimization efficiency while maintaining the simplicity of the framework.

Accelerating reaction optimization through data-rich experimentation and machine-assisted process development

“…In contrast, traditional Bayesian optimization approaches are typically sequential in nature, although ongoing research is addressing this limitation. 41,42 Specific SNOBFIT algorithm details for both case studies, including parameter settings and implementation specifics, can be found in the ESI †…”