Structure and Dynamics of Deep Eutectic Systems from Cluster-Optimized Energy Functions

TL;DR

This study develops and validates machine learning-based energy functions for eutectic mixtures of water, acetamide, and KSCN, accurately capturing thermodynamic, structural, spectroscopic, and transport properties through a cluster-optimized approach.

Contribution

It introduces a novel cluster-based method combined with electronic structure calculations and machine learning to generate predictive energy functions for complex heterogeneous systems.

Findings

Models accurately reproduce experimental viscosity and spectroscopic decay times.

Both TIP3P and TIP4P water models yield favorable simulation results.

Including acetamide improves viscosity predictions at low water content.

Abstract

Generating energy functions for heterogeneous systems suitable for quantitative and predictive atomistic simulations is a challenging undertaking. The present work combines a cluster-based approach with electronic structure calculations at the density functional theory level and machine learning-based energy functions for a spectroscopic reporter for eutectic mixtures consisting of water, acetamide and KSCN. Two water models are considered: TIP3P which is consistent with the CGenFF energy function and TIP4P which - as a water model - is superior to TIP4P. Both fitted models, {\bf M2$^{\rm TIP3P}$} and {\bf M2$^{\rm TIP4P}$}, yield favourable thermodynamic, structural, spectroscopic and transport properties from extensive molecular dynamics simulations. In particular, the slow and fast decay times from 2-dimensional infrared spectroscopy and the viscosity for water-rich mixtures are described realistically and consistent with experiments. On the other hand, including the co-solvent (acetamide) in the present case is expected to further improve the computed viscosity for low-water content. It is concluded that such a cluster-based approach is a promising and generalizable route for routine parametrization of heterogeneous, electrostatically dominated systems.