20.500.12556/RUL-185518 Predicting density and elucidating the thermodynamic drivers of viscosity in carboxy-functionalized imidazolium ionic liquids 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. carboxy-functionalized imidazolium ion idensity functional theory symmetry-adapted perturbation theory machine learning ion–ion interactions ionic liquid smart design karboksi-funkcionaliziran imidazolijev ion teorija gostotnega funkcionala simetriji prilagojena perturbacijska teorija strojno učenje interakcije med ioni načrtovanje ionskih tekočin s ciljnimi lastnostmi true false true Angleški jezik Slovenski jezik Članek v reviji 2026-08-07 08:04:48 2026-08-07 08:04:50 2026-08-08 04:08:07 0000-00-00 00:00:00 2026 0 0 17 str. iss. 14, art. 2495 Vol. 31 Jul. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 544.6.018.4 1420-3049 10.3390/molecules31142495 287193091 RAZ_Cako_Bagany_Nikolett_2026.pdf RAZ_Cako_Bagany_Nikolett_2026.pdf 1 3AAB9B85A44808B7ED1F380645894D58 9998676a16c103e9a337caf33c9d513a9ed75ab435912aa35ced3f1f483ec46f 8d519252-9226-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=244080 https://www.mdpi.com/1420-3049/31/14/2495 1 8f17aa40-9225-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=244079 Fakulteta za kemijo in kemijsko tehnologijo 0 0 0