Machine learning modeling of the CO₂ solubility in ionic liquids by using σ-profile descriptors

The solubility of carbon dioxide (CO₂) in solvents is important for carbon capture and utilization technologies, with ionic liquids (ILs) being promising due to their ability to capture CO₂. Since the number of possible ILs is huge, predicting CO₂ solubility during solvent screening is essential. In this work, various machine learning (ML) models including, multiple linear regression, artificial neural network, and random forest, were developed by using 9864 data points covering 124 ILs and descriptors from the σ-profile for predicting CO₂ solubility in ILs. The random forest model produced the best performance (R² = 0.9754, MAE = 0.0257). We estimated the importance of the descriptors, highlighting that those with non-polar characteristics of the σ-profile are important. Lastly, we predicted CO₂ solubilities for 1444 unstudied ILs. The combination of ML with the σ-profile descriptors offers great generalizability for predicting CO₂ solubility in ILs. This enables IL screening for CO₂ related applications.