The accurate prediction of ionic solvation phenomena is essential for applications across numerous fields, including materials science, 1,2 renewable energy, pharmaceuticals, and environmental science. Such predictions play key roles in the optimization of chemical processes and prediction of behavior for biological systems. In particular, the solvation free energy is fundamentally related to useful physicochemical properties. These include solubilities, acid dissociation constants (pK a ), partition coefficients, and others. A combination for neutral solutes, which served as bottlenecks in compiling large sets of solvation free energies for ions. The IonSolv-Aq database introduced by Zheng et al. , with reported standard uncertainties of 2.5 kcal mol -1 , focused on hydration free energies of 118 anions and 155 cations using only experimentally-derived thermodynamic properties. Their study suggested that systematic deviations between experimental data and computed predictions (from the COSMO-RS and SMD solvation models) for ionic solutes can be corrected using empirical parameters. Despite the contributions of these databases (MNSol, DISSOLVE, and IonSolv-Aq), the number of entries remains insufficient for comprehensive parameterization of solvation models and development of accurate data-driven models.