SAFT-Guided Viscosity Modeling of Hydrophobic Deep Eutectic Solvents - Methods and Pitfalls

Predicting liquid viscosity remains challenging because it arises from complex, multiscale phenomena: strong, composition-dependent intermolecular forces (including hydrogen bonding and van der Waals interactions), microstructural organization in non-ideal mixtures, and strong sensitivity to temperature and pressure. These features limit transferability of force fields and make purely empirical correlations unreliable for novel formulations such as hydrophobic deep eutectic solvents (HDES). Here we present a simple, thermodynamics-based guideline to estimate the parameters needed for viscosity prediction of HDES. Our workflow first fits SAFT-VR Mie parameters to experimental density data, then optimizes viscosity-relevant parameters via an Entropy-Scaling model that leverages the chosen equation of state together with density and viscosity measurements. The approach is implemented using open-source tools (Clapeyron.jl and EntropyScaling.jl, available on GitHub), facilitating reproducible parameter estimation. Results show the method is effective at producing consistent viscosity predictions across HDES formulations; however, careful prescription and validation of SAFT parameters are essential to avoid propagated errors. This thermodynamic route offers a practical balance between physical fidelity and simplicity for screening and design of HDES.