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It is based on the codification, of the atomic inter\u2010component interactions, cation\/gas and anion\/gas, which are used to obtain a pattern of activation in a <jats:italic>Kohonen<\/jats:italic> 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<jats:sub>2<\/jats:sub> and O<jats:sub>2<\/jats:sub> in 1\u2010butyl\u20103\u2010methylimidazolium hexafluorophosphate (BMIM PF<jats:sub>6<\/jats:sub>) at identical temperature and pressure (TP) conditions, The effect of variable cation and anion effect has been evaluated.<\/jats:p>","DOI":"10.1002\/cphc.202100632","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T08:37:36Z","timestamp":1630399056000},"page":"2190-2200","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["The Solubility of Gases in Ionic Liquids: A Chemoinformatic Predictive and Interpretable Approach"],"prefix":"10.1002","volume":"22","author":[{"given":"Gon\u00e7alo V. S. 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