The solvation free energy of a solute in scCO 2 , which directly correlates with its solubility in the fluid, was estimated using a previously developed approach based on cDFT. In this framework, the grand thermodynamic potential of the system is expressed as a functional Beyond inherent model simplifications, such as coarsening the internal chemical structure of the solute and solvent molecules, a major challenge is determining the critical parameters of the solutes, as most compounds degrade thermally before reaching the temperatures of interest. To predict critical temperatures, pressures, and enthalpies of vaporization, a multitask learning (MTL) GCNN model was additionally employed, augmented with molecular descriptors accounting for intermolecular interactions. The MTL model outperformed singletask GCNN:RDKit models but was less accurate than the CatBoost:CDK models (Table ).