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

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.

Chemical similarity of dialkyl carbonates and carbon dioxide opens an avenue for novel greenhouse gas scavengers: cheap recycling and low volatility via experiments and simulations

“…2,3 Since the sources of CO 2 emissions are versatile and abundant, various types of CO 2 scavengers are necessary. [4][5][6][7] Several parameters which are of key technological importance must be optimized. Their interplays and tradeoffs must be systematically accounted for.…”

The Melting Point Profile of Organic Molecules: A Chemoinformatic Approach

“…MOLMAP approach consists of mapping the structural features of a given chemical system on a Kohonen neural network [ 7 ] based on the profile of properties representing each structural unit. Atoms, [ 8 ] bonds, [ 9 ] and atomic inter‐component interactions [ 10 ] represent different structural features used to model viscosity, chemical reactivity, and gas solubility, respectively. MOLMAP is a generical form of codification that permits to find similarities between different structural units; however, with resemblant chemical environments and, in opposite direction, encodes differences between apparently similar structural features in the context of different chemical environments.…”

Prediction of the Phase Composition Profile of Three‐Compound Mixtures in Liquid‐Liquid Equilibrium: A Chemoinformatics Approach

“…MOLMAPs have been applied in different studies such as the classification of chemical reactions without assignment of reaction centers, [18] conversion of descriptor's dimensionality in QSAR applications, [19] chemical reactivity evaluation from databases with no negative data, [20] classification of metabolic reactions, [21,22] mutagenicity prediction, [23] QSAR analysis of phenolic antioxidants, [24] viscosity classification [25,26] and gas solubility in ILs. [27] Here the machine learning protocol involved Random Forest models [28,29] that receive information about the mixture (molecules and their relative amount), the temperature and the pressure, and predict the molar fraction of one component of the mixture (one of the two molecules) in a specific phase. Different models were trained for the IL-rich and IL-poor phases.…”

“…This approach enables to compare systems of different nature, including different number of components, and can take into account the molar fractions of each chemical. MOLMAPs have been applied in different studies such as the classification of chemical reactions without assignment of reaction centers, [18] conversion of descriptor's dimensionality in QSAR applications, [19] chemical reactivity evaluation from databases with no negative data, [20] classification of metabolic reactions, [21,22] mutagenicity prediction, [23] QSAR analysis of phenolic antioxidants, [24] viscosity classification [25,26] and gas solubility in ILs [27] …”