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

Understanding electrochemical interfaces at a microscopic level is essential for elucidating important electrochemical processes in electrocatalysis, batteries, and corrosion. While 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 Letter, 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 polarization in electrolytes and nonlocal 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 polarization effects. Our Letter lays the foundation for atomistic modeling of complex, realistic electrochemical interfaces using machine learning potential at ab initio accuracy.