Investigating the role of ionic liquids in CO2 electrochemical reduction: AI meets quantum chemistry

Abstract. Recently, ionic liquids (ILs) have garnered remarkable attention as electrolytes for CO2 electrochemical reduction (CO2ER) due to their unique properties viz. thermal and chemical stability, good CO2 solubility, and their potential to reduce overpotential. While many researchers have explored the catalytic performance of ILs in CO2ER, a comprehensive understanding of the parameters affecting the catalytic performance is still absent. Experimental methods for evaluating the catalytic performance have limitations, given the unclear understanding of the reaction mechanism. Recently, Artificial Intelligence (AI) methods have gained increased attention across diverse applications including chemical engineering. These methods play a pivotal role in extracting insights, understanding patterns, and mitigating uncertainty within datasets. In this study, we leverage AI for investigating the critical factors affecting the CO2ER catalytic performance via Gibbs free energy and capacity. We formulate a novel dataset of 30 electronic and geometric properties of 90 ILs using the Conductor-like Screening Model for Realistic Solvents (COSMO-RS) and TURBOMOLE. Despite the conventional literature that emphasizes the impact of anions on CO2 solubility and the conventional association of cations as co-catalysts proximal to the negatively charged electrode, our findings underscore the indispensable role of anions, specifically those featuring Sulphur (S), Fluorine (F), and Oxygen (O), in influencing both CO2 solubility and catalytic processes, despite their relatively far distance from the electrode. Moreover, our study provides an explanation for the difference between solubility trends and catalytic activity, focusing on interaction types. These outcomes will contribute to the more effective selection of ionic liquids for CO2ER.