Accurate prediction of the physical properties of mixed systems is essential for efficient
industrial design and process optimization. This study explores the use of a hybrid approach …

The accurate prediction of physical properties is critical for the successful application of both
conventional and novel chemicals across various industries. This work focuses on predictive …

The vast diversity of ionic liquids (ILs) necessitates accurate predictive models to facilitate
their applications in industry. This study integrates machine learning (ML) algorithms with …

This work conducts a comprehensive modeling study on the viscosity, density, heat capacity,
and surface tension of ionic liquid (IL)‐IL binary mixtures by combining the group …

Physicochemical properties of ionic liquids (ILs) are essential in solvent screening and
process design. However, due to their vast diversity, acquiring IL properties through …

Predicting the viscosity of ionic liquids (ILs)-water mixtures precisely is considerable for
diversity applications in chemical industries. In this work, interpretable machine learning …

Abstract Properties of ionic liquids (ILs) play a crucial role in solvent design, process design,
and process simulation for their various industrial applications. However, ILs come in a wide …

Ionic liquids (ILs), because of the advantages of low volatility, good thermal stability, high
gas solubility and easy recovery, can be regarded as the green substitute for traditional …

Viscosity is one of the most important fundamental properties of fluids. However, accurate
acquisition of viscosity for ionic liquids (ILs) remains a critical challenge. In this study, an …

In this work, a nonlinear model that integrates the group contribution (GC) method with a
well-known machine learning algorithm, ie, artificial neural network (ANN), is proposed to …