Machine Learning–Driven Discovery of Sustainable Ionic Liquids for CO ₂ Capture from Large Chemical Spaces

High Resolution Image Download MS PowerPoint Slide Ionic liquids (ILs) represent one of the most promising and versatile solvent families for CO 2 capture due to their structural tunability and low volatility. However, the sheer scale and complexity of the IL chemical design space make the discovery of high-performance candidates extraordinarily challenging. In this work, we introduce a SHAP-guided Domain Filter (SGDF) framework that leverages machine learning (ML) to address this challenge, enabling reliable large-scale screening across millions of potential IL structures. Starting from over 32 million cation–anion combinations, sequential SGDF gates based on melting point ( T m ), toxicity, viscosity, and CO 2 solubility reduce the search space by more than 2 orders of magnitude, ultimately yielding 69,009 promising candidates. Experimental validation is performed on 12 ILs, including two model-selected candidates that exhibit the desired properties. Notably, the two model-selected ILs exhibit the highest CO 2 absorption capacities, surpassing all other samples. Across the 12 ILs evaluated, the framework achieves high predictive accuracy (RMSE = 0.046, MAE = 0.030, R 2 = 0.781), showing excellent agreement between predicted and experimental results. These findings demonstrate that combining predictive modeling with experimental validation offers both mechanistic insight and a scalable, data-driven pathway for designing next-generation sustainable ILs for CO 2 capture.