Improved modeling of battery electrolytes: betting on model fitting or quantum effects?

The accurate modeling of solvent dynamics and ionic interactions is of crucial importance for the development of novel electrolytes in next-generation metal-ion batteries. This study presents a critical evaluation of the semi-classical computational approach, the adaptive Quantum Thermal Bath (adQTB) method, as a methodology for capturing the key properties of glyme-based solvents and their Ca2+-based electrolyte solutions. Simulations reveal that the adQTB method is particularly effective in accurately reproducing vibrational spectra, while offering good transferability across systems and conditions without requiring empirical parameter adjustments. In the context of electrolyte solutions, semi-classical adQTB simulations in combination with graph theory analysis indicate a distribution of the various charge-carrying clusters that is closely aligned with the conductivity measurements previously reported [Nguyen et al., Phys. Chem. Chem. Phys., 2022, 24, 21601], in sharp contrast to the empirically scaled force field. These findings emphasize the necessity of incorporating nuclear quantum effects for reliable electrolyte modeling, thereby paving the way for the advancement of post-Li battery technology.