Efficient molecular dynamics simulations of deep eutectic solvents with first-principles accuracy using machine learning interatomic potentials

In recent years, deep eutectic solvents (DESs) emerged as highly tunable and eco-friendly alternatives to common organic solvents and liquid electrolytes. In the present work, the ability of machine learning (ML) interatomic potentials for molecular dynamics (MD) simulations of these liquids is explored, showcasing a trained neural network interatomic potential for a 1:2 ratio mixture of choline chloride and urea (reline). Using the ML potentials trained on density-functional theory (DFT) data, MD simulations for large systems of thousands of atoms and nanoseconds-long time scales are feasible at a fraction of the computational cost of the target first-principles simulations. The obtained structural and dynamical properties of reline from MD simulations using our machine learning models are in good agreement with the first-principles MD simulations and experimental results. Running a single MD simulation is highlighted as a general shortcoming of typical first-principles studies if the dynamical properties are investigated. Furthermore, velocity cross-correlation functions are employed to study the collective dynamics of the molecular components in reline.