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

Nahoko Kuroki; Yuki Suzuki; Daisuke Kodama; Firoz Alam Chowdhury; Hidetaka Yamada; Hirotoshi Mori

doi:10.1021/acs.jpcb.2c07305

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

J Phys Chem B. 2023 Mar 9;127(9):2022-2027. doi: 10.1021/acs.jpcb.2c07305. Epub 2023 Feb 24.

Authors

Nahoko Kuroki^{1

2}, Yuki Suzuki³, Daisuke Kodama³, Firoz Alam Chowdhury⁴, Hidetaka Yamada^{4

5}, Hirotoshi Mori¹

Affiliations

¹ Department of Applied Chemistry, Faculty of Science and Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan.
² Japan Science and Technology Agency, ACT-X, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan.
³ Department of Chemical Biology and Applied Chemistry, College of Engineering, Nihon University, 1 Nakagawara, Tokusada, Tamura-machi, Koriyama, Fukushima 963-8642, Japan.
⁴ Research Institute of Innovative Technology for the Earth, 9-2 Kizugawadai, Kizugawa, Kyoto 619-0292, Japan.
⁵ Frontier Science and Social Co-creation Initiative, Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa 920-1192, Japan.

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.