Geometric features were chosen to describe the amount of void space and the connection between the ion and the directly coordinated atoms, such as relative distances of coordinating atoms to the ion, buried volume of the coordinating atoms, and coordination number. Electronic features were chosen to describe the local electronic and chemical environment in the coordination sphere, such as ESP, partial (i.e., Hirshfeld) charge, and average local ionization energy. To account for correlation between features, we performed recursive feature elimination (RFE) with 5-fold crossvalidation using a random forest model as the estimator to arrive at a reduced feature set for each ion exchange pair (Supporting Information). Using the reduced feature set for each ion pair dataset, we tested four different ML models: LASSO, ridge regression, random forest, and kernel ridge regression (KRR), with the KRR model performing the best (Figure ). This superior performance of KRR is likely due to its ability to handle non-linear relationships, unlike LASSO and ridge regression, and remain stable for a large number of features relative to number of data samples, unlike random forest. The performance of the selected KRR model using the RFE-reduced feature set, as assessed using the mean absolute error (MAE) and R 2 , is comparable to the model trained on the full feature set (Supporting Information Figure ). All ion pair models other than the Li + to K + exchange pair have ≤ 3 kcal/mol MAE and all models have > 0.7 R 2 values (Figure , Supporting Information Figures ). The large ranges of the DEtotal for the datasets span from 33 kcal/mol to 105 kcal/mol depending on the ion pair, hence the scaled MAEs are all quite small at ≤ 0.06 except for the Li + to K + exchange pair (Figure ).