Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents

Carbon dioxide (CO₂) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO₂ capture. Chemically reactive DESs, which form chemical bonds with the CO₂, are superior to nonreactive, physically based DESs for CO₂ absorption. However, there are no accurate computational models that provide accurate predictions of the CO₂ solubility in chemically reactive DESs. Here, we develop machine learning (ML) models to predict the solubility of CO₂ in chemically reactive DESs. As training data, we collected 214 data points for the CO₂ solubility in 149 different chemically reactive DESs at different temperatures, pressures, and DES molar ratios from published work. The physics-driven input features for the ML models include σ-profile descriptors that quantify the relative probability of a molecular surface segment having a certain screening charge density and were calculated with the first-principle quantum chemical method COSMO-RS. We show here that, although COSMO-RS does not explicitly calculate chemical reaction profiles, the COSMO-RS-derived σ-profile features can be used to predict bond formation. Of the models trained, an artificial neural network (ANN) provides the most accurate CO₂ solubility prediction with an average absolute relative deviation of 2.94% on the testing sets. Overall, this work provides ML models that can predict CO₂ solubility precisely and thus accelerate the design and application of chemically reactive DESs.