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

Abstract

Carbon dioxide (CO 2 ) 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 CO 2 capture. Chemically reactive DESs, which form chemical bonds with the CO 2 , are superior to nonreactive, physically based DESs for CO 2 absorption. However, there are no accurate computational models that provide accurate predictions of the CO 2 solubility in chemically reactive DESs. Here, we develop machine learning (ML) models to predict the solubility of CO 2 in chemically reactive DESs. As training data, we collected 214 data points for the CO 2 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 CO 2 solubility prediction with an average absolute relative deviation of 2.94% on the testing sets. Overall, this work provides ML models that can predict CO 2 solubility precisely and thus accelerate the design and application of chemically reactive DESs.

Machine learning models accelerate deep eutectic solvent discovery for the recycling of lithium-ion battery cathodes

“…It is a powerful tool to accelerate molecule design and functional materials discovery and processing for applications such as catalysts, 20 pharmaceutical synthesis, 21 and pretreatment of Li-ion batteries. 22,23 ML approaches also have great performance in DESs, such as property prediction 24 and gas absorption, 25 which could unlock new opportunities for the recovery of spent LIBs through DESs. Great attention has been paid to leaching cathodes through DESs; 12 currently, the mainstream view assumes that low viscosity, high acidity, strong coordination, and reducibility of DESs might be beneficial for efficient leaching, 26 while little work has validated these hypotheses, and we are still lacking quantification of the importance of each property.…”