Abstract
This study uses ab initio and machine learning-based molecular dynamics simulations to explore solvation structures and ion dynamics in CaCl2 aqueous electrolytes. We identify multiple solvation structures around Ca2+ 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 Ca2+ 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.
| Original language | English |
|---|---|
| Article number | 141985 |
| Journal | Chemical Physics Letters |
| Volume | 867 |
| DOIs | |
| State | Published - May 16 2025 |
Funding
This research was supported by the Joint Center for Energy Storage Research (JCESR), a US Department of Energy, Energy Innovation Hub. This manuscript has been authored by UT-Battelle, LLC, under contract DEAC05-00OR22725 with the US Department of Energy (DOE). This work was supported as part of the Center for Steel Electrification by Electrosynthesis (C-STEEL), an Energy Earthshot Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences (BES) and Advanced Scientific Computing Research (ASCR). Z.Y. acknowledges the computing resources provided on UAHPC, a high-performance computing cluster operated by the University of Alabama.
Keywords
- CaCl aqueous electrolytes
- Ion dynamics
- Machine learning potentials
- Metadynamics
- Molecular dynamics simulations
- Solvation structures