Machine Learning-Boosted Design of Ionic Liquids for CO2 Absorption and Experimental Verification.

Abstract

Efficient CO2 capture is indispensable for achieving a carbon-neutral society while maintaining a high quality of life. Since the discovery that ionic liquids (ILs; room-temperature molten salts) can absorb CO2, various solvents composed of molecular ions have been studied. However, it is challenging to observe the properties of each isolated ion component to control the function of ILs as they are mixtures of ions. Finding the optimal cation-anion combination for the CO2 absorbent from their enormous chemical space had been impossible in a practical sense. This study applied electronic structure informatics to explore ILs with high CO2 solubility from 402,114 IL candidates. The feature variables were determined by a set of cheap quantum chemistry calculations for isolated small-ion fragments, and the importance of molecular geometries and electronic states governing molecular interactions was identified via the wrapper method. As a result, it was clearly shown that the electronic states of ionic species must have essential roles in the CO2 physisorption capacity of ILs. Considering synthetic easiness for the candidates narrowed by the machine learning model, trihexyl(tetradecyl)phosphonium perfluorooctanesulfonate was synthesized. Using a magnetic suspension balance, it was experimentally confirmed that this IL has higher CO2 solubility than trihexyl(tetradecyl)phosphonium bis(trifluoromethanesulfonyl)amide, which is the previous best IL for CO2 absorption.