Robust design of optimal solvents for chemical reactions—A combined experimental and computational strategy
Abstract Solvents can have significant effects on chemical reactions, however, their huge number makes the selection very difficult and costly. This work presents a systematic method for the design of reaction solvents. Kinetic models are built by correlating experimentally determined reaction rate constants in a small set of known solvents with corresponding solvent theoretical descriptors determined from quantum chemical calculations. Optimal solvents are then identified from the solution of an optimization-based molecular design problem. Besides the deterministic optimization, a robust solvent design framework is proposed to identify solvents that possess the best reaction performance under model uncertainties. The methodology is exemplified for a competitive Diels–Alder reaction with the objective of maximizing the production of the desired product relative to that of the byproduct. Compared to the best experimentally identified solvent, a 10.9% improvement in reaction performance can be achieved with our designed solvent. It is proven that the proposed method is an efficient tool for fast identification of high-performance solvents for chemical reactions. Moreover, it potentially promotes the development of new solvents.