Quantitative structure-property relationship techniques for predicting carbon dioxide solubility in ionic liquids using machine learning methods

Abstract

Ionic liquids (ILs) are considered unique and attractive types of solvents with great potential to capture carbon dioxide (CO₂) and reduce its emissions into the atmosphere. On the other hand, carrying out experimental measurements of CO₂ solubility for each new IL is time-consuming and expensive. Whereas, the possible combinations of cations and anions are numerous. Therefore, the preparation and design of such processes requires simple and accurate models to predict the solubility of CO₂ as a greenhouse gas. In the present study, two different models, namely: artificial neural network (ANN) and support vector machine optimized with dragonfly algorithm (DA-SVM) were used in order to establish a quantitative structure–property relationship (QSPR) between the molecular structures of cations and anions and the CO₂ solubility. More than 10 116 CO₂ solubility data measured in various ionic liquids (ILs) at different temperatures and pressures were collected. 13 significant PaDEL descriptors (E2M, MATS8S, TDB6I, TDB1S, ATSC4V, MATS8M, ATSC7V, Gats2S, Gats5S, Gats5C, ATSC6V, DE, and Lobmax), temperature and pressure were considered as the model input data. For the test set data (2023 data point), the estimated mean absolute error (MAE) and R² for the ANN model are of 0.0195 and 0.9828 and 0.0219 and 0.9745 for the DA-SVM model. The results obtained showed that both models can reliably predict the solubility of CO₂ in ILs with a slight superiority of the ANN model. Examination of sensitivity and outlier diagnosis examinations confirmed that the QSPR model optimized using the ANN algorithm is better suited to correlate and predict this property.