Gas-to-ionic liquid partition: QSPR modeling and mechanistic interpretation

Area of Science:

• Physical Chemistry
• Computational Chemistry
• Chemical Informatics

Background:

• Ionic liquids (ILs) are versatile solvents with tunable properties.
• Accurate prediction of gas-to-ionic liquid partition coefficients (log KILA) is crucial for designing IL-based separation processes.

Purpose of the Study:

• To develop and validate quantitative structure-property relationship (QSPR) models for predicting log KILA.
• To establish interpretable linear and nonlinear models based on molecular descriptors.

Main Methods:

• Development of linear models using electrostatic potential and matrix-based descriptors.
• Utilization of Gaussian processes for nonlinear model construction.
• Rigorous validation including cross-validation and applicability domain assessment.

Main Results:

• An optimal four-parameter linear model (1Ed) was established using descriptors related to electrostatic potential, matrix properties, and dipole moment.
• The developed models demonstrated high predictive accuracy and reliability across multiple datasets.
• The applicability domain analysis confirmed the models' ability to predict log KILA for structurally diverse solutes.

Conclusions:

• The QSPR models provide a reliable and interpretable method for predicting gas-to-ionic liquid partition coefficients.
• The adopted methodology is broadly applicable for QSPR modeling in the field of ionic liquids.
• These findings facilitate the rational design and selection of ionic liquids for various applications.