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Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials.
Avula, Nikhil V S; Klein, Michael L; Balasubramanian, Sundaram.
Affiliation
  • Avula NVS; Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.
  • Klein ML; Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States.
  • Balasubramanian S; Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.
J Phys Chem Lett ; 14(42): 9500-9507, 2023 Oct 26.
Article in En | MEDLINE | ID: mdl-37851540
ABSTRACT
The diffusivity of water in aqueous cesium iodide solutions is larger than that in neat liquid water and vice versa for sodium chloride solutions. Such peculiar ion-specific behavior, called anomalous diffusion, is not reproduced in typical force field based molecular dynamics (MD) simulations due to inadequate treatment of ion-water interactions. Herein, this hurdle is tackled by using machine learned atomic potentials (MLPs) trained on data from density functional theory calculations. MLP based atomistic MD simulations of aqueous salt solutions reproduce experimentally determined thermodynamic, structural, dynamical, and transport properties, including their varied trends in water diffusivities across salt concentration. This enables an examination of their intermolecular structure to unravel the microscopic underpinnings of the differences in their transport properties. While both ions in CsI solutions contribute to the faster diffusion of water molecules, the competition between the heavy retardation by Na ions and the slight acceleration by Cl ions in NaCl solutions reduces their water diffusivity.

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Phys Chem Lett Year: 2023 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Phys Chem Lett Year: 2023 Document type: Article Affiliation country:
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