Solvation structures and ion dynamics of CaCl2 aqueous electrolytes using metadynamics and machine learning molecular dynamics simulations

This study uses ab initio and machine learning-based molecular dynamics simulations to explore solvation structures and ion dynamics in CaCl 2 aqueous electrolytes . We identify multiple solvation structures around Ca 2+ ions, influencing water molecule orientation extending to second hydration shell and residence times of water molecules in first hydration shell. The self-diffusivities of ions and water molecules, as calculated in machine learning-based molecular dynamics simulations, closely align with experimental measurements. Additionally, we analyze Ca 2+ ion transitions across ballistic, subdiffusive, and diffusive regimes by analyzing angle distribution histograms and van Hove correlation function, providing a comprehensive understanding of the underlying molecular interactions . • Identified diverse solvation structures around the Ca 2+ ion, enhancing understanding of ion-water interactions. • Employed advanced ML-MD simulations to predict and analyze complex ion dynamics with high accuracy. • Explored ion dynamics across multiple regimes, providing insights into the transport properties of ions. • Demonstrated the potential of MD simulations with ML potentials to reduce computational costs while maintaining accuracy.