The Solubility of Gases in Ionic Liquids: A ChemoinformaticPredictive and Interpretable Approach

This work comprises the study of solubilities of gases in ionicliquids (ILs) using a chemoinformatic approach. It is based onthe codification, of the atomic inter-component interactions,cation/gas and anion/gas, which are used to obtain a pattern ofactivation in a Kohonen Neural Network (MOLMAP descriptors). A robust predictive model has been obtained with the Random Forest algorithm and used the maximum proximity as aconfidence measure of a given chemical system compared to the training set. The encoding method has been validated with molecular dynamics. This encoding approach is a valuable estimator of attractive/repulsive interactions of a generical chemical system IL+gas. This method has been used as a fast/visual form of identification of the reasons behind the differences observed between the solubility of CO2and O2in 1-butyl-3-methylimidazolium hexafluorophosphate (BMIM PF6) at identical temperature and pressure (TP) conditions, The effect of variable cation and anion effect has been evaluated.