Robust design of optimal solvents for chemical reactions-A combined experimental and computational strategy

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.