Ion-Specific Anomalous Water Diffusion in Aqueous Electrolytes: A Machine-Learned Many-Body Force Field Study with MACE
Massimo Ciacchi, Ilnur Saitov, Nico Di Fonte, Isabella Daidone, Carlo Pierleoni

TL;DR
This study uses a machine-learned many-body force field within the MACE framework to accurately simulate ion-specific anomalous water diffusion in electrolyte solutions, matching experimental observations and revealing microscopic mechanisms.
Contribution
It introduces a reliable MLFF trained on DFT data that improves simulation accuracy of water diffusion in electrolytes, especially for NaCl and CsI solutions.
Findings
Reproduces experimentally observed water diffusion anomalies.
Shows stronger Na$^{+}$--water interactions improve simulation accuracy.
Identifies I$^{-}$ as key to water acceleration in CsI solutions.
Abstract
The dynamics of water in electrolyte solutions exhibits a striking, ion-specific anomaly: the diffusion coefficient of water is enhanced relative to the neat liquid in chaotropic CsI solutions, yet suppressed in kosmotropic NaCl solutions. This phenomenon, long challenging for classical force-field-based molecular dynamics, is studied here using classical molecular dynamics simulations with a many-body machine-learned force field (MLFF) trained within the MACE equivariant graph neural network framework. The force field is trained on energies, forces, and stresses computed at the density functional theory level with the revPBE-D3 exchange--correlation functional, which provides a reliable balance between accuracy and computational efficiency for aqueous systems. Simulations of NaCl and CsI aqueous solutions at ambient conditions over a concentration range of 0.89--3.56 mol/kg reproduce…
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