Naturally, the solubility of CO 2 is a key property to optimize for carbon capture processes. Therefore, robust, accurate, and efficient models that predict the CO 2 solubility as a function of DES composition and operating conditions are needed. Different thermodynamic models have been successfully developed to model different properties . However, these methods are difficult to develop and parameterize. An alternative consists of using quantum chemistry-based models, such as COSMO-RS , for predicting the solubility of CO 2 in DESs . This method has been used to obtain so-called sigma profiles for DES components, which have been used as descriptors to train machine learning models such as artificial neural networks (ANN). Sigma profiles describe the charge distribution across the molecule surface area and are obtained from Density Functional theory (DFT) calculations . Unfortunately, the computation of sigma profiles via DFT is computationally expensive and hinders the ability to conduct quick screening for solvents.