A Molecular Dynamics Assisted Machine Learning Approach to Locate the Transition Point of a Mixture of Deep Eutectic Solvent and Water

Deep eutectic solvents (DES) are highly viscous, which hinders their applications in different areas. Their viscosities can be reduced by adding a small amount of water. However, when an excessive amount of water is added, the DES loses its inherent structure and becomes an aqueous solution of DES components. The concentration at which the transition from “water in DES” to “DES in water” occurs can be defined as the transition point of that particular DES. When the mixture of DES and water is used for any application, it is very important that the original supramolecular structure is retained. Hence, it will be beneficial to have the value of the transition point. We have proposed a method for finding the transition point with the help of classical molecular dynamics simulations and machine learning. Various structural and dynamical properties of DES-water mixtures at different mole fractions of water are calculated and fitted using machine learning methods to locate the transition point. The lowest transition point obtained from all of the properties can be treated as the lower limit for the water-rich phase.