Machine Learning for Ionic Liquids in CO2 Conversion: Advances, Challenges, and Perspectives
The rapid increase in atmospheric CO2 due to fossil-fuel consumption has heightened the demand for efficient carbon capture and utilization technologies. Ionic liquids (ILs) have emerged as versatile media and catalysts for CO2 conversion, offering advantages such as negligible volatility, wide electrochemical windows, and strong CO2 affinity. However, the vast design space of ILs and limited experimental data make traditional trial-and-error screening inefficient. This review summarizes recent advancements in applying machine learning (ML) to the design and screening of ILs for CO2 conversion. The roles of ILs in catalytic processes and the limitations of traditional screening methods are discussed. ML-based workflows are explored, with emphasis on addressing challenges posed by small and noisy datasets. Finally, future opportunities in mechanism-informed descriptors, multi-objective optimization, and the integration of domain expertise with data-driven approaches are highlighted to accelerate the discovery of next-generation ILs for sustainable CO2 conversion.