Machine Learning for Ionic Liquids in CO2 Conversion: Advances, Challenges, and Perspectives

• • The IL chemical space is estimated to encompass up to 10^18 potential structures, making exhaustive experimental screening intractable; ML-driven high-throughput screening can reduce candidate ILs by orders of magnitude, accelerating discovery of efficient catalysts for CO2 conversion. • • Traditional trial-and-error methods are inefficient due to the strong dependence of IL properties (e.g., viscosity, density) on ion structure, which critically influences reaction kinetics and selectivity; ML models trained on existing data can predict these properties with high accuracy, enabling rational design. • • Small and noisy datasets are a major bottleneck; data augmentation and pre-processing techniques (e.g., as reviewed in Ref. 79) can improve model robustness, but careful validation is required to avoid overfitting. • • Integration of domain expertise with data-driven approaches, including mechanism-informed descriptors and multi-objective optimization, is essential to achieve both high activity and selectivity in CO2 conversion, as highlighted in the review.