Solvent Effects in Organic Reaction: From Empirical Scales to Multiscale Modeling and Data‐Driven Optimization †

Abstract

Solvent effects permeate essentially all solution‐phase chemistry. Yet, a unified and generally predictive description remains challenging because solvent effects span multiple length and time scales: bulk dielectric screening and viscosity capture only part of the physics, while first‐shell coordination, ion pairing, hydrophobic aggregation, and heterogeneous structural fluctuations can be decisive near the reaction coordinate; moreover, when solvent relaxation occurs on time scales comparable to chemical events, nonequilibrium solvation and memory effects may invalidate static free energy corrections. This review outlines a practical trajectory from early phenomenology to modern computation and, more recently, data‐driven decision making. We first revisit the conceptual roots of solvent effects and the rise of empirical solvent scales that compress complex microscopic behavior into usable descriptors. We then summarize the logic of implicit, explicit, and hybrid solvent models in contemporary computational chemistry, emphasizing their assumptions, cost‐accuracy trade‐offs, and applicability. Finally, we discuss data standardization and benchmarking for solvation modeling, learning‐based prediction of properties and solvation free energies, machine‐learned potentials that enable scalable explicit solvent simulations, and emerging workflows for solvent/condition recommendation and closed‐loop optimization.