Experimental Compilation and Computation of Hydration Free Energies for Ionic Solutes

Although charged solutes are common in many chemical systems, traditional solvation models perform poorly in calculating solvation energies of ions. One major obstacle is the scarcity of experimental data for solvated ions. In this study, we release an experiment-based aqueous ionic solvation energy data set, IonSolv-Aq, that contains hydration free energies for 118 anions and 155 cations, more than 2 times larger than the set of hydration free energies for singly charged ions contained in the 2012 Minnesota Solvation Database commonly used in benchmarking studies. We discuss sources of systematic uncertainty in the data set and use the data to examine the accuracy of popular implicit solvation models COSMO-RS and SMD for predicting solvation free energies of singly charged ionic solutes in water. Our results indicate that most SMD and COSMO-RS modeling errors for ionic solutes are systematic and correctable with empirical parameters. We discuss two systematic offsets: one across all ions and one that depends on the functional group of the ionization site. After correcting for these offsets, solvation energies of singly charged ions are predicted using COSMO-RS to 3.1 kcal mol(-1) MAE against a challenging test set and 1.7 kcal mol(-1) MAE (about 3% relative error) with a filtered test set. The performance of SMD is similar, with MAE against those same test sets of 2.7 and 1.7 kcal mol(-1). These results underscore the importance of compiling larger experimental data sets to improve solvation model parametrization and fairly assess performance.

Automated summary

This study presents an experiment-based aqueous ionic solvation energy dataset, IonSolv-Aq, which is 2 times larger than the commonly used 2012 Minnesota Solvation Database. The results indicate that the popular implicit solvation models COSMO-RS and SMD can be corrected for systematic errors in predicting solvation free energies of singly charged ionic solutes in water. These findings emphasize the importance of larger experimental data sets for improving solvation model parametrization and evaluating performance.