Revisiting the Paradigm of Reaction Optimization in Flow with a Priori Computational Reaction Intelligence

Abstract

The use of micro/meso‐fluidic reactors has resulted in both new scenarios for chemistry and new requirements for chemists. Through flow chemistry, large‐scale reactions can be performed in drastically reduced reactor sizes and reaction times. This obvious advantage comes with the concomitant challenge of re‐designing long‐established batch processes to fit these new conditions. The reliance on experimental trial‐and‐error to perform this translation frequently makes flow chemistry unaffordable, thwarting initial aspirations to revolutionize chemistry. By combining computational chemistry and machine learning, we have developed a model that provides predictive power tailored specifically to flow reactions. We show its applications to translate batch to flow, provide mechanistic insight, contribute reagent descriptors, and to synthesize a library of novel compounds in excellent yields after executing a single set of conditions.