Chemoinformatics-Driven Design of New Physical Solvents for Selective CO₂ Absorption

The removal of CO₂ from gases is an important industrial process in the transition to a low-carbon economy. The use of selective physical (co-)solvents is especially perspective in cases when the amount of CO₂ is large as it enables one to lower the energy requirements for solvent regeneration. However, only a few physical solvents have found industrial application and the design of new ones can pave the way to more efficient gas treatment techniques. Experimental screening of gas solubility is a labor-intensive process, and solubility modeling is a viable strategy to reduce the number of solvents subject to experimental measurements. In this paper, a chemoinformatics-based modeling workflow was applied to build a predictive model for the solubility of CO₂ and four other industrially important gases (CO, CH₄, H₂, and N₂). A dataset containing solubilities of gases in 280 solvents was collected from literature sources and supplemented with the new data for six solvents measured in the present study. A modeling workflow based on the usage of several state-of-the-art machine learning algorithms was applied to establish quantitative structure-solubility relationships. The best models were used to perform virtual screening of the industrially produced chemicals. It enabled the identification of compounds with high predicted CO₂ solubility and selectivity toward other gases. The prediction for one of the compounds, 4-methylmorpholine, was confirmed experimentally.