Reaction Kinetic Model Considering the Solvation Effect Based on the FMO Theory and Deep Learning

Reaction solvents are capable of significantly enhancing the kinetic rate and selectivity of chemical reactions through the solvation effects. However, the commonly used reaction kinetic models considering the solvation effects are based on the transition state theory, which is generally limited to the demanding exploration of transition states. In this paper, a reaction solvent screen framework is established for screening potential reaction solvents, where a novel reaction kinetic model is proposed based on the frontier molecular orbital theory correlating the reaction rate constants with the numbers of hydrogen bond donors/acceptors of solvents and the solvation HOMO/LUMO energies of reactants, which quantify the solvation effects on reaction systems independent of transition states and are calculated through the implicit solvation model using the solvent dielectric constants. To facilitate high-throughput screening of reaction solvents in a wide search space, a tailor-made text-based deep learning model is then developed for fast and accurate predictions of dielectric constants. Finally, two case studies, namely, SNAr and Menschutkin reactions, are presented to demonstrate the feasibility and effectiveness of the proposed reaction solvent screen framework.

Automated summary

This study presents a new reaction solvent screening framework, establishing a reaction kinetic model based on frontier molecular orbital theory and using an implicit solvation model to quantify the solvent effects on reaction systems independent of transition states. By this method, high-throughput screening of reaction solvents is achieved, and the feasibility and effectiveness of the framework are demonstrated in two case studies.