Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response

TL;DR

This paper introduces a hybrid machine learning framework capable of simulating complex electrochemical interfaces with ab initio accuracy, addressing dielectric response challenges in metals and electrolytes.

Contribution

A novel hybrid machine learning potential that models dielectric response and charge transfer at electrochemical interfaces, enabling realistic simulations of complex systems.

Findings

Reproduced the bell-shaped differential Helmholtz capacitance at Pt(111)/electrolyte interface.

Calculated dielectric profiles revealing electronic polarization effects.

Validated the method's accuracy against known electrochemical phenomena.

Abstract

Understanding electrochemical interfaces at a microscopic level is essential for elucidating important electrochemical processes in electrocatalysis, batteries and corrosion. While \textit{ab initio} simulations have provided valuable insights into model systems, the high computational cost limits their use in tackling complex systems of relevance to practical applications. Machine learning potentials offer a solution, but their application in electrochemistry remains challenging due to the difficulty in treating the dielectric response of electronic conductors and insulators simultaneously. In this work, we propose a hybrid framework of machine learning potentials that is capable of simulating metal/electrolyte interfaces by unifying the interfacial dielectric response accounting for local electronic polarisation in electrolytes and non-local charge transfer in metal electrodes. We validate our method by reproducing the bell-shaped differential Helmholtz capacitance at the Pt(111)/electrolyte interface. Furthermore, we apply the machine learning potential to calculate the dielectric profile at the interface, providing new insights into electronic polarisation effects. Our work lays the foundation for atomistic modelling of complex, realistic electrochemical interfaces using machine learning potential at \textit{ab initio} accuracy.