Key Takeaways & Executive Findings
- •• • 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.
Abstract
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.
1. Introduction
Industrial activities and extensive fossil-fuel consumption have caused a persistent rise in global CO2 emissions, with annual emissions reaching an estimated 36.2 Gt in 2024, positioning CO2 as a primary driver of accelerated climate change. Confronting the severe consequences of global warming, such as sea-level rise and extreme weather events, demands urgent and innovative technology solutions. Among these, carbon capture and storage (CCS) and carbon capture and utilization (CCU) technologies have emerged as critical pathways toward climate neutrality. While conventional CCS faces challenges related to high cost and CO2 leakage risk, CCU represents a transformative opportunity by converting CO2 into value-added chemicals and fuels. Catalytic CO2 conversion can reduce the chemical industry's reliance on fossil resources and contribute to closing the carbon cycle through sustainable pathways, offering vital technological support for global carbon neutral ambitions.
Nevertheless, the inherent thermodynamic stability and kinetic inertness of the CO2 molecule pose fundamental challenges, necessitating the development of highly efficient catalytic systems to overcome significant reaction energy barriers. Ionic liquids (ILs) have emerged as promising candidates due to their unique properties, including low volatility, high thermal and chemical stability, and outstanding structural tunability. In CO2 conversion, ILs exhibit strong CO2 adsorption capacity and can serve as catalysts, co-catalysts, or advanced reaction media in electrocatalysis, photocatalysis, and thermal catalytic processes. However, the rational selection and design of optimal ILs for specific conversion pathways remain a formidable challenge. The physicochemical properties of ILs, such as viscosity and density, strongly depend on the chemical structures of their constituent anions and cations, critically influencing reaction kinetics, selectivity, and overall catalytic efficiency. Navigating the immense chemical space of possible ion combinations, estimated to encompass up to 10^18 potential structures, is intractable through experimental trial-and-error alone. This review addresses this bottleneck by summarizing recent advancements in applying machine learning to the design and screening of ILs for CO2 conversion, emphasizing strategies to overcome small and noisy datasets and highlighting future opportunities for accelerating discovery.
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Jiaming Zheng, Yingjie Zhou, Feng Yan (2026). Machine Learning for Ionic Liquids in CO2 Conversion: Advances, Challenges, and Perspectives. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-026-4151-2
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Frequently Asked Questions
What are the main challenges in applying machine learning to ionic liquid design for CO2 conversion, and how can they be addressed?
The primary challenges include the vast chemical space (up to 10^18 potential ion combinations) and limited experimental data, which often results in small and noisy datasets. To address these, the review emphasizes data pre-processing and augmentation techniques (e.g., as discussed in Ref. 79) to improve model robustness. Additionally, incorporating mechanism-informed descriptors and domain expertise can help guide feature engineering and model selection, while multi-objective optimization can balance competing targets such as activity and selectivity.
How do ionic liquids contribute to CO2 conversion, and what specific properties make them suitable?
Ionic liquids possess negligible volatility, wide electrochemical windows, and strong CO2 affinity, making them effective as catalysts, co-catalysts, or reaction media in various conversion processes. Their structural tunability allows optimization of properties like viscosity and density, which critically influence reaction kinetics and selectivity. For instance, strong CO2 adsorption capacity enhances local CO2 concentration, improving conversion efficiency.
What are the limitations of traditional trial-and-error screening methods for ionic liquids, and how does machine learning overcome them?
Traditional screening is inefficient due to the enormous number of possible ion combinations and the time-consuming, costly nature of experimental synthesis and testing. Machine learning can rapidly predict IL properties and performance from existing data, enabling high-throughput virtual screening. This reduces the number of candidates requiring experimental validation, accelerating the discovery of optimal ILs for specific CO2 conversion pathways.
What future directions are highlighted for integrating machine learning with ionic liquid research?
The review highlights three key directions: (1) development of mechanism-informed descriptors that capture the underlying physics and chemistry of IL-CO2 interactions; (2) multi-objective optimization to simultaneously optimize multiple performance metrics (e.g., activity, selectivity, stability); and (3) integration of domain expertise with data-driven approaches to ensure models are physically plausible and generalizable. These strategies aim to accelerate the discovery of next-generation ILs for sustainable CO2 conversion.
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