The Solubility of Gases in Ionic Liquids: A Chemoinformatic Predictive and Interpretable Approach
Gonçalo V. S. M. Carrera, João Miguel Inês, Carlos E. S. Bernardes, Kyrylo Klimenko, Karina Shimizu, José N. Canongia Lopes
Abstract
Abstract This work comprises the study of solubilities of gases in ionic liquids (ILs) using a chemoinformatic approach. It is based on the codification, of the atomic inter‐component interactions, cation/gas and anion/gas, which are used to obtain a pattern of activation 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 a confidence 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 CO 2 and O 2 in 1‐butyl‐3‐methylimidazolium hexafluorophosphate (BMIM PF 6 ) at identical temperature and pressure (TP) conditions, The effect of variable cation and anion effect has been evaluated.