in pK a values from previous literature estimates. ). Some solvents have significant outliers; many of those correspond to "rare" acid sites such as carbon acids or nitrogen-centered acids, or small and unusual compounds such as ethenimine or hydrogen peroxide. Other significant multi-solvent pK a models have been trained, both using data from iBonD: the XGBoost and neural network models by We also benchmarked the ML model against the data and semi-QM calculations in the Zheng/nonoverlap dataset, which includes 2 amino acids in methanol and 1 in ethanol. The semi-QM method estimates pK a values in non-aqueous solvents using a thermochemical cycle, experimental aqueous pK a data, and solvation free energies computed with COSMO-RS. The method includes fitting an empirical parameter δ which accounts for ∆G * solv (H + ) as well as any remaining systematic error. The performance of the ML and semi-QM methods for these was comparable, with the semi-QM method leading to 0.90 and 1.34 pK a unit RMSEs in methanol and ethanol, respectively, versus 1.03 and 0.92 pK a units for the ML model.