Abstract
Offering a compelling combination of safety and cost-effectiveness, water-in-salt (WiS) electrolytes have emerged as promising frontiers in energy storage technology. Still, there is a strong demand for research and development efforts to make these electrolytes ripe for commercialization. Here, we present a first-principles-based molecular dynamics (MD) study addressing in detail the properties of a sodium triflate WiS electrolyte for Na-ion batteries. We have developed a workflow based on a machine learning (ML) potential derived from ab initio MD simulations. As ML potentials are typically restricted to the interpolation of the data points of the training set and have hardly any predictive properties, we subsequently optimize a classical force field based on physics principles to ensure broad applicability and high performance. Performing and analyzing detailed MD simulations, we identify several very promising properties of the sodium triflate as a WiS electrolyte but also indicate some potential stability challenges associated with its use as a battery electrolyte.
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