Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials DOI Creative Commons
Nikhil V. S. Avula, Michael L. Klein, Sundaram Balasubramanian

et al.

arXiv (Cornell University), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

The diffusivity of water in aqueous cesium iodide solutions is larger than that neat liquid water, and vice versa for sodium chloride solutions. Such peculiar ion-specific behavior, called anomalous diffusion, not reproduced typical force field-based molecular dynamics (MD) simulations due to inadequate treatment ion-water interactions. Herein, this hurdle tackled using machine learned atomic potentials (MLPs) trained on data from density functional theory calculations. MLP-based atomistic MD salt reproduce experimentally determined thermodynamic, structural, dynamical, transport properties, including their varied trends diffusivities across concentration. This enables an examination intermolecular structure unravel the microscopic underpinnings distinction transport. While both ions CsI contribute faster diffusion molecules, competition between heavy retardation by Na-ions slight acceleration Cl-ions NaCl reduces diffusivity.

Language: Английский

Unveiling the Role of Polypropylene Interactions with Lithium Fluoride Solutions: Insights into Crystallization Dynamics and Membrane Behaviour DOI
Giuseppe Prenesti, Alfredo Cassano, Agostino Lauria

et al.

Journal of Membrane Science, Journal Year: 2025, Volume and Issue: unknown, P. 123905 - 123905

Published: Feb. 1, 2025

Language: Английский

Citations

0
On the Physical Origins of Reduced Ionic Conductivity in Nanoconfined Electrolytes DOI Creative Commons
Kara D. Fong, Clare P. Grey, Angelos Michaelides

et al.

ACS Nano, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Ion transport through nanoscale pores is at the heart of numerous energy storage and separation technologies. Despite significant efforts to uncover complex interplay ion–ion, ion–water, ion–pore interactions that give rise these processes, atomistic mechanisms ion motion in confined electrolytes remain poorly understood. In this work, we use machine learning-based molecular dynamics simulations characterize with first-principles-level accuracy aqueous NaCl graphene slit pores. We find ionic conductivity decreases as degree confinement increases, a trend governed by changes both self-diffusion dynamic ion–ion correlations. show coefficients our ions are strongly influenced overall electrolyte density, which nonmonotonically height based on layering water molecules within pore. further observe shift ions' diffusion mechanism toward more vehicular increases. ubiquity ideal solution (Nernst–Einstein) assumptions field, nonideal contributions become pronounced under confinement. This increase correlations arises not simply from an fraction associated ions, commonly assumed, but pair lifetimes. By building mechanistic understanding transport, work provides insights could guide design nanoporous materials optimized for efficient selective transport.

Language: Английский

Citations

0
Free energy profiles for chemical reactions in solution from high-dimensional neural network potentials: The case of the Strecker synthesis DOI
Alea Miako Tokita, Timothée Devergne, A. Marco Saitta

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(17)

Published: May 6, 2025

Machine learning potentials (MLPs) have become a popular tool in chemistry and materials science as they combine the accuracy of electronic structure calculations with high computational efficiency analytic potentials. MLPs are particularly useful for computationally demanding simulations such determination free energy profiles governing chemical reactions solution, but to date, applications still rare. In this work, we show how umbrella sampling can be combined active high-dimensional neural network (HDNNPs) construct systematic way. For example first step Strecker synthesis glycine aqueous provide detailed analysis improving quality HDNNPs datasets increasing size. We find that, addition typical quantification force errors respect underlying density functional theory data, long-term stability convergence physical properties should rigorously monitored obtain reliable converged solution.

Language: Английский

Citations

0
Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials DOI Creative Commons
Nikhil V. S. Avula, Michael L. Klein, Sundaram Balasubramanian

et al.

arXiv (Cornell University), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

The diffusivity of water in aqueous cesium iodide solutions is larger than that neat liquid water, and vice versa for sodium chloride solutions. Such peculiar ion-specific behavior, called anomalous diffusion, not reproduced typical force field-based molecular dynamics (MD) simulations due to inadequate treatment ion-water interactions. Herein, this hurdle tackled using machine learned atomic potentials (MLPs) trained on data from density functional theory calculations. MLP-based atomistic MD salt reproduce experimentally determined thermodynamic, structural, dynamical, transport properties, including their varied trends diffusivities across concentration. This enables an examination intermolecular structure unravel the microscopic underpinnings distinction transport. While both ions CsI contribute faster diffusion molecules, competition between heavy retardation by Na-ions slight acceleration Cl-ions NaCl reduces diffusivity.

Language: Английский

Citations

0