It is important to acknowledge the generally high prediction error for new compounds; however, such a result is expected, as achieving precise predictive accuracy remains challenging even for large datasets, including those involving aqueous solubility. Experimental methods for determining solubility in scCO 2 are known to be time-and resource-intensive, and the accuracy of such measurements is influenced by numerous factors. These include the precision of state parameter measurements (e.g., pressure and temperature), control of phase equilibrium, the choice of measurement method (static, dynamic, or gravimetric), and the purity of the solute. Additionally, phenomena such as polymorphism and chemical interactions between CO 2 and the solute 62,63 can significantly affect solubility, which must be carefully considered when developing predictive models. These factors collectively contribute to high experimental uncertainty, with substantial inter-laboratory variability. This variability may cast doubt on the reliability of reported solubility data in scCO 2 . Since the inherent error is embedded in the data itself, an aleatoric limit is established. Attempts to improve predictive performance on such data may lead to overfitting, compromising the model's generalizability.