We developed and evaluated Graph Neural Network (GNN) models for predicting each property (D-MPNN/FFNN as implemented in Chemprop v2 ), providing insights into their relative strengths and limitations and discussing future research directions for modeling the complex chemistries of ionic solutes. To our knowledge, this work represents the first time that a machine learning model was developed for predicting gas-phase acidities and solvation free energies of anions.