Structures and Ion Transport Properties of Hydrate-Melt Electrolytes: A Machine-Learning Potential Molecular Dynamics Study.

Abstract

High-concentration aqueous electrolytes (hydrate-melts) have attracted significant attention for lithium-ion batteries due to their nonflammability and low toxicity. In these electrolytes, the static and dynamic structures of the solvent play a crucial role in determining various properties, such as the ionic conductivity, of the system. To clarify the solvent structure and ion diffusion mechanism, we conducted molecular dynamics simulations using a machine learning potential for Li and Na hydrate-melts. By analyzing the dynamical interaction between ions and their coordinating molecules, we found the ligand exchange of H₂O molecules coordinated to cations occurs frequently. As a result, it is considered that the kinetic energy of H₂O is transferred to cations and drives the diffusion of cations in the hydrate-melts. This ion transport mechanism is different from the conventionally understood vehicle-type or hopping-type ion transport mechanism. The comparison of Na hydrate-melts and Li hydrate-melts shows the higher diffusion of Na relative to Li. It was suggested that there exists an optimal value for the strength of interaction between cations and H₂O molecules, which influences ion diffusion, and that the interaction for Na is close to this optimal value compared to that of the Li.