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Self-Optimizing Flow Reactions for Sustainability: An Experimental Bayesian Optimization Study
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DOI：10.1021/acssuschemeng.4c03253.png)
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摘要
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Self-optimizing flow reactors have received significant attention in recent years, with Bayesian optimization (BO) being identified as the most effective method for reaction optimization. However, there are many different approaches using BO algorithms, which is overwhelming for experimentalists. Here, using pharmaceutically relevant amide coupling reactions, we explore best practices in three areas, to promote the efficient design of sustainable processes: (1) A high extent of exploration in an optimization algorithm was deemed necessary to ensure a good design space overview. (2) Yield was optimized within a small experimental budget, while minimizing environmental impact, by setting up an objective function with penalties (e.g., for excess reagent usage). (3) An optimization algorithm using an auxiliary data set appeared to behave well for the same substrates using a different coupling reagent, but provided no advantage when using substrates with substantially lower reactivity. We envisage that these general recommendations will aid flow chemists utilizing BO for automated development of sustainable reactions.
Keyword:
flow chemistry
automation
process development
Bayesian optimization
benchmarking
期刊
IF:
7.3
论文数:
1.7W
被引数:
10.7W
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