Unveiling the structure and dynamic properties of NaCl aqueous solutions across wide concentration and temperature ranges: Insights from deep potential molecular dynamics

Understanding the local structure and dynamic properties of electrolyte solutions under different thermodynamic conditions is essential to advancing electrochemical systems. Here, we employed a deep potential molecular dynamics method, trained on first-principles data, to investigate aqueous sodium chloride solutions within a concentration range of 1.2–6.0 M and a temperature range of 270–350 K. Our results demonstrate satisfactory accuracy in reproducing first-principles structural and dynamic features. Our detailed analysis revealed the concentration dependence of the ionic hydration shell layer, the solvent hydrogen bonding network, and the ionic pairing behavior. We also quantified the changes in ionic diffusion coefficients, water residence time, and ionic conductivity under different conditions. Our results validate deviations from ideal Stokes–Einstein and Nernst–Einstein relationships at high concentrations and capture Arrhenius temperature dependence of ion diffusion. This study confirms the predictive power of machine learning potential models in electrolyte systems and provides an important theoretical basis for ion–solvent interactions, which are crucial in electrochemical modeling.