User-assisted methodology targeted for building structure interpretable QSPR models for boosting CO2 capture with ionic liquids

Abstract

Task-specific ionic liquid (IL) is an emerging class of compounds that may be environmentally friendly. Properly selected, these compounds may be green alternative to amine solutions and can replace them in post-combustion carbon dioxide (CO2) capture processes on an industrial scale. However, owing to the vast diversity of ions and their possible combinations, laboratory research is time consuming and expensive. Therefore, computational methods are preferred for assessing their potential applications. In this study, three quantitative structure–property relationship models based on six distinct descriptors were created to predict Henry’s law constant (HLC) of CO2 in 62 ILs. The statistical parameters of multiple linear regression, logistic regression, and partial least squares models were satisfactory. In all cases, the coefficients of determination (R2) exceeded 0,90, and both external and internal validation proved them to be reliable and predictive with Q2 and Rpred2 values exceeding 0,90 and 0,87, respectively. Three of the descriptors were attributed to cations, and three were attributed to anions. In contrast to many other models, the descriptors were chosen in a manner that ensured their interpretability. Each of the six descriptors was analyzed for its influence on HLC. On this basis, guidelines for designing the structure of ionic liquids with increased CO2 absorption capacity were developed.