Deriving effective electrode-ion interactions from free-energy profiles at electrochemical interfaces

Abstract

Understanding ion adsorption at electrified metal-electrolyte interfaces is essential for accurate modeling of electrochemical systems. Here, we systematically investigate the free energy profiles of Na${}^{+}$, Cl${}^{-}$, and F${}^{-}$ ions at the Au(111)-water interface using enhanced sampling molecular dynamics with both classical force fields and machine-learned interatomic potentials (MLIPs). Our classical metadynamics results reveal a strong dependence of predicted ion adsorption on the Lennard-Jones parameters, highlighting that -- without due care -- standard mixing rules can lead to qualitatively incorrect descriptions of ion-metal interactions. We present a systematic methodology for tuning the cross-term LJ parameters to control adsorption energetics in agreement with more accurate models. As a surrogate for an ab initio model, we employed the recently released Universal Models for Atoms (UMA) MLIP, which validates classical trends and displays strong specific adsorption for chloride, weak adsorption for fluoride, and no specific adsorption for sodium, in agreement with experimental and theoretical expectations. By integrating molecular-level adsorption free energies into continuum models of the electric double layer, we show that specific ion adsorption substantially alters the interfacial ion population, the potential of zero charge, and the differential capacitance of the system. Our results underscore the critical importance of force field parameterization and advanced interatomic potentials for the predictive modeling of ion-specific effects at electrified interfaces and provide a robust framework for bridging molecular simulations and continuum electrochemical models.

21 pages, 13 figures, Supplementary Information available

References in corpus (19)

• Canonical sampling through velocity-rescaling
• Escaping free-energy minima
• PLUMED 2: New feathers for an old bird
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• SchNet - a deep learning architecture for molecules and materials
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• PLUMED: a portable plugin for free-energy calculations with molecular dynamics
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• A molecular perspective of water at metal interfaces
• A practical guide to machine learning interatomic potentials -- Status and future
• Molecular Simulation of Electrode-Solution Interfaces
• Simulating electrochemical systems by combining the finite field method with a constant potential electrode
• A semiclassical Thomas-Fermi model to tune the metallicity of electrodes in molecular simulations
• Size-dependence of hydrophobic hydration at electrified gold/water interfaces
• Effect of the metallicity on the capacitance of gold - aqueous sodium chloride interfaces
• A QM/MD coupling method to model the ion-induced polarization of graphene
• Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response
• IRA: A shape matching approach for recognition and comparison of generic atomic patterns
• Unsupervised learning of representative local atomic arrangements in molecular dynamics data