For the first method, we neglect the temperature variation of the dissolution enthalpy (∆H diss,T ≈ ∆H diss,298K ). This assumption results in Eq. 8 and is valid as long as the temperature T does not deviate too much from 298K. For the calculation of ∆H diss,298K , we need the sublimation enthalpy at 298K (∆H sub,298K ) and the solvation enthalpy at 298K (∆H solv,298K ). Abraham and Acree 30 developed a correlation to calculate ∆H sub,298K based on solute parameters as shown in Eq. 10. The first four solute parameters (E, S, A, B) can be predicted with the machine learning models developed during our earlier work, and the solute parameter V can be calculated using the McGowan method. The additional parameters introduced by Abraham and Acree, 30 i.e. I(OH,adj), I(OH,non), and I(NH) in Eq. 10, are indicator variables for the presence of aliphatic diols with adjacent OH groups, aliphatic diols with non-adjacent OH groups, and aliphatic amine groups, respectively. In our earlier work, we also published a model for the prediction of ∆H solv,298K . Even though a good model performance was achieved, to obtain an improved performance for solutes with a higher molar mass, a new model is built for ∆H solv,298K using a transfer learning methodology similar to the one we used for the prediction of ∆G solv,298K . In the supporting information (section S.3) more information can be found on this transfer learning model architecture and the quantum chemical and experimental databases employed.