Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide–Ionic Liquid Mixtures

This study addresses a critical gap in the existing literature on carbon dioxide and ionic liquid (IL) mixtures, where fragmented and incomplete data, particularly for flow properties, hinder practical applications. Therefore, this work aimed to establish a robust and efficient method for predicting the density of the CO₂-IL mixtures across diverse operating conditions and IL families using novel validation techniques. Both linear and symbolic regression models provided relevant insights but failed to accurately capture the IL-CO₂ interactions in a mixture that determine the molar volume of CO₂ at infinite dilution when solubilized by a given IL. Therefore, more mathematically flexible artificial neural networks (ANN) were trained based on three different sets of features: (1) IL critical properties, (2) IL structural descriptors, and (3) a selective combination of (1) and (2). While all models showed relative deviations consistently below 3% for the testing data, combining critical and structural data significantly improved accuracy (<i>R</i>² = 0.986, testing data set). A postprocessing outlier-handling method enhanced model performance, removing a minimal fraction (below 0.2%) of unphysical data points. Furthermore, molecular dynamics simulations validated the robust generalization of all ANN models, with the combined model exhibiting remarkable accuracy over operating conditions outside the training ranges for ILs in the training set and even for ILs that are not included in this data set. This computational approach provides a significantly faster and broader alternative to other thermodynamical tools, establishing a solid method for future machine learning (ML)-based property prediction augmented by external validation from cross-comparison tests and statistical thermodynamics models.