The Solubility of Gases in Ionic Liquids: A Chemoinformatic Predictive and Interpretable Approach. Issue 21 (20th September 2021) - Carrera, Gonçalo V. S. M.; Inês, João; Bernardes, Carlos E. S.; Klimenko, Kyrylo; Shimizu, Karina; Canongia Lopes, José N.
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 CO2 and O2 in 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. Abstract : A chemoinformatic approach based on the codification of all the pairs atomic intercomponent interactions between cation/gas and anion/gas (Ionic Liquid+Gas) in a Kohonen neural network (MOLMAPs) is presented in this work. The form of codification, validated by molecular dynamics, is a first valuable tool to highlight the most attractive and repulsive interactions responsible for a concrete solubility of a given gas in an ionic liquid.