Predicting density and elucidating the thermodynamic drivers of viscosity in carboxy-functionalized imidazolium ionic liquids

Cáko Bagány, Nikolett (author), Armaković, Sanja J. (author), Armaković, Stevan (author), Šarac, Bojan (author), Belić, Sanja (author), Cerc Korošec, Romana (author), Bešter-Rogač, Marija (author), Gadžurić, Slobodan (author)

The rational design of ionic liquids (ILs) is often hindered when promising candidates, such as carboxy-functionalized imidazolium chlorides, exhibit properties like extreme viscosity that preclude direct experimental measurement. In this study, we synthesized a series of these ILs and addressed this “experimental gap” with a combined computational strategy. For the few liquids accessible to measurement, we obtained density, viscosity, and conductivity data. For the majority, we turned to atomistic modeling and machine learning. Symmetry-adapted perturbation theory (SAPT2) energy decomposition uncovered the dominance of electrostatic interactions in governing viscosity, an insight obscured by total binding energies from DFT. In addition, a recently developed machine learning model, named IonIL-IM-D1, predicted the density of [C$_2$COOHeim][Cl] with an error of less than 1% upon validation, though experimental verification for the solid candidates was not possible. This predictive framework was extended to propose and evaluate new IL candidates, offering a complementary strategy for exploring macroscopic behavior when direct experimental measurements are not feasible.

• Language: English
• Keywords: carboxy-functionalized imidazolium ion, idensity functional theory, symmetry-adapted perturbation theory, machine learning, ion–ion interactions, ionic liquid smart design
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FKKT - Faculty of Chemistry and Chemical Technology
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 17 str.
• Numbering: Vol. 31, iss. 14, art. 2495
• PID: 20.500.12556/RUL-185518
• UDC: 544.6.018.4
• ISSN on article: 1420-3049
• DOI: 10.3390/molecules31142495
• COBISS.SI-ID: 287193091
• Publication date in RUL: 07.08.2026

Record is a part of a journal

• Title: Molecules
• Shortened title: Molecules
• Publisher: MDPI
• ISSN: 1420-3049
• COBISS.SI-ID: 18462981

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: karboksi-funkcionaliziran imidazolijev ion, teorija gostotnega funkcionala, simetriji prilagojena perturbacijska teorija, strojno učenje, interakcije med ioni, načrtovanje ionskih tekočin s ciljnimi lastnostmi