Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials

TL;DR

This study uses machine learned potentials to accurately simulate water diffusion in electrolyte solutions, revealing ion-specific effects and microscopic mechanisms behind anomalous diffusion behaviors.

Contribution

It introduces machine learned atomic potentials trained on DFT data to reproduce complex ion-water interactions and anomalous diffusion phenomena in electrolyte solutions.

Findings

MLPs accurately reproduce experimental properties.

Ion-specific effects influence water diffusivity.

Microscopic analysis explains anomalous diffusion trends.

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 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 of water diffusivities across salt concentration. This enables an examination of their intermolecular structure to unravel the microscopic underpinnings of the distinction in their transport. While both ions in CsI solutions contribute to faster diffusion of water molecules, the competition between the heavy retardation by Na-ions and slight acceleration by Cl-ions in NaCl solutions reduces their water diffusivity.