We then applied permutation feature importance to all four models trained on the RFEreduced features to obtain a measure of the importance of each feature. This feature selection method assesses the decrease in model performance when individual feature values are permuted in order to assign the significance of each feature. From the two feature categories, we determine the ratio of geometric and electronic feature weights for the best-performing KRR model as well as the remaining models (Figure and Supporting Information Figure ). We find that there are significant contributions from both electronic and geometric features in all of the models. Neither the electronic nor geometric feature groups alone are informative enough to achieve the predictive accuracy of a model with both types of coordination characteristics (Supporting Information Figure ). These results are consistent with the slightly less accurate LASSO, ridge, and random forest models as well as the KRR model trained on the full feature set rather than the RFE-reduced feature set (Supporting Information Figures and). Although the K + to Na + system shows a. b.