Machine Learning Prediction of Henry’s Law Constant for CO2 in Ionic Liquids and Deep Eutectic Solvents

Abstract

Ionic liquids (ILs) and deep eutectic solvents (DESs) have been extensively studied as absorbents for CO2 capture, demonstrating high efficiency in this role. To optimize the search for compounds with superior absorption properties, theoretical approaches, including machine learning methods, are highly relevant. In this study, machine learning models were developed and applied to predict Henry’s law constants for CO2 in ILs and DESs, aiming to identify systems with the best absorption performance. The accuracy of the models was assessed in interpolation tasks within the training set and extrapolation beyond its domain. The optimal predictive models were built using the CatBoost algorithm, leveraging CDK molecular descriptors for ILs and RDKit descriptors for DESs. To define the applicability domain of the models, the SHAP-based leverage method was employed, providing a quantitative characterization of the descriptor space where predictions remain reliable. The developed models have been integrated into the web platform chem-predictor, where they can be utilized for predicting absorption properties.