QSPR Modeling of Liquid‐liquid Equilibria in Two‐phase Systems of Water and Ionic Liquid

The increasing application of new ionic liquids (IL) creates the need of liquid‐liquid equilibria data for both miscible and quasi‐immiscible systems. In this study, equilibrium concentrations at different temperatures for ionic liquid+water two‐phase systems were modeled using a Quantitative‐Struct...

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Veröffentlicht in:Molecular informatics 2020-09, Vol.39 (9), p.e2000001-n/a
Hauptverfasser: Klimenko, Kyrylo Oleksandrovych, Inês, João Miguel, Esperança, José Manuel Silva Simões, Rebelo, Luís Paulo Nieto, Aires‐de‐Sousa, João, Carrera, Gonçalo Valente Silva Mariño
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container_issue 9
container_start_page e2000001
container_title Molecular informatics
container_volume 39
creator Klimenko, Kyrylo Oleksandrovych
Inês, João Miguel
Esperança, José Manuel Silva Simões
Rebelo, Luís Paulo Nieto
Aires‐de‐Sousa, João
Carrera, Gonçalo Valente Silva Mariño
description The increasing application of new ionic liquids (IL) creates the need of liquid‐liquid equilibria data for both miscible and quasi‐immiscible systems. In this study, equilibrium concentrations at different temperatures for ionic liquid+water two‐phase systems were modeled using a Quantitative‐Structure‐Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. Models were subjected to the Y‐scrambling test for robustness assessment. The best models have also been validated using an external test set that is not part of the ILThermo database. A two‐phase equilibrium diagram for one of the external test set IL is presented for better visualization of the results and potential derivation of tie lines.
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In this study, equilibrium concentrations at different temperatures for ionic liquid+water two‐phase systems were modeled using a Quantitative‐Structure‐Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. 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source Wiley Online Library Journals Frontfile Complete
subjects Data analysis
Data points
ILThermo
Ionic liquids
Ions
Miscibility
Modelling
outlier detection
Outliers (statistics)
Phase diagrams
Phase equilibria
Solvents
Temperature dependence
title QSPR Modeling of Liquid‐liquid Equilibria in Two‐phase Systems of Water and Ionic Liquid
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