Structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine learning interatomic potential simulations

Deep Eutectic Solvents have recently gained significant attention as versatile and inexpensive materials with many desirable properties and a wide range of applications. In particular, their similar characteristics to ionic liquids, make them a promising class of liquid electrolytes for electrochemical applications. In this study, we utilized a local equivariant neural network interatomic potential model to study a series of deep eutectic electrolytes based on lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) by molecular dynamics (MD) simulations. The use of equivariant features combined with the strict locality result in highly accurate, data-efficient and scalable interatomic potentials enabling large-scale MD simulations of these liquids with first-principles accuracy. Comparing the structure of the liquids to reported results from classical force field (FF) simulations indicates that ion–ion interactions are not accurately characterized by FFs. Furthermore, close contacts between lithium ions bridged by oxygen atoms of two amide molecules are observed. The computed cationic transport numbers and the estimated ratios of Li–amide lifetime (τ[Li–amide]) to the amide’s rotational relaxation time (τ[R]), combined with the ionic conductivity trend, suggest a more structural Li+ transport mechanism in the LiTFSI:urea mixture through exchange of amide molecules. However, a vehicular transport could have a larger contribution to Li+ ion transport in the LiTFSI:N-methylacetamide electrolyte. Moreover, comparable diffusivities of Li+ cation and TFSI – anion and a τ[Li–amide]/τ[R] close to unity, indicate that vehicular and solvent-exchange mechanisms have rather equal contributions to Li+ ion transport in the LiTFSI:acetamide system.