Machine learning revolutionizes design of green solvents for carbon capture: a new era for ionic liquid development

With climate change posing an unprecedented global challenge, the demand for environmentally friendly solvents in green chemical processes and carbon dioxide capture has surged. Ionic liquids (ILs) have emerged as promising "designer solvents" due to their negligible volatility, broad liquid temperature range, and exceptional thermal stability. However, the immense chemical space of ILs—with theoretically up to 10¹⁸ possible cation-anion combinations—has created a critical bottleneck in efficient screening and design. Traditional experimental methods are costly and time-consuming, while theoretical calculations like molecular dynamics and quantum chemistry remain computationally prohibitive for large-scale screening. This urgent need for accelerated discovery has set the stage for a transformative technological leap.

A comprehensive review published in Frontiers in Energy by researchers from Nanjing Tech University systematically evaluates the application of machine learning (ML) in ionic liquid design. The study maps the evolution from traditional trial-and-error methods to artificial intelligence-driven quantitative structure-property relationship (QSPR) modeling. The team examined cutting-edge ML techniques including neural networks, random forests, support vector machines, and Gaussian process regression, while emphasizing the critical role of molecular descriptor selection—from simple group contributions to complex quantum chemistry-based features. A key innovation highlighted is the integration of molecular dynamics (MD) and density functional theory (DFT) calculations with ML models to create interpretable "white-box" predictions that reveal underlying physical mechanisms.

The review demonstrates that ML-based QSPR models achieve remarkable predictive accuracy across critical IL properties. For viscosity prediction, deep neural networks attained R² values exceeding 0.99 on datasets of over 8,600 data points. In CO₂ solubility screening, graph neural networks outperformed traditional methods with R² = 0.9884, while an integrated CatBoost model achieved R² = 0.9925 by combining group contribution and molecular structure descriptors. The study also showcases multi-objective optimization frameworks that simultaneously balance viscosity, toxicity, and absorption capacity—successfully identifying 37 optimal ILs from a pool of 1,420 candidates. Notably, ML models reduced process optimization time from 1,218 seconds to just 4.12 seconds compared to conventional simulation methods.

This work establishes a systematic framework that fundamentally transforms IL development from serendipitous discovery to predictive design. By bridging experimental data, theoretical computations, and AI algorithms, the approach enables rapid screening of millions of candidate solvents while providing atomic-level insights into structure-property relationships. The implications are profound: accelerating the deployment of sustainable solvents for carbon capture, advancing green chemistry applications, and facilitating the design of safer electrolytes for next-generation batteries. As the world races toward carbon neutrality, this ML-driven paradigm offers a powerful toolkit to unlock the full potential of ionic liquids in building a sustainable chemical industry, potentially reducing development cycles from years to months while minimizing environmental impact through computational prioritization.

Article Link

https://link.springer.com/article/10.1007/s11708-025-1011-7

https://rdcu.be/eQgK7