Accelerating and Enhancing Thermodynamic Simulations of Electrochemical Interfaces.

ABSTRACT: Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsorbates, and electrolyte. Predicting stable surface structures remains challenging, as traditional surface Pourbaix diagrams tend to either rely on expert knowledge or costly ab initio sampling and neglect thermodynamic equilibration with the environment. Machine learning (ML) potentials can accelerate static modeling but often overlook dynamic surface transformations. Here, we extend the Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) method to autonomously sample surface reconstructions modeled under aqueous electrochemical conditions. Through fine-tuning foundational ML force fields, we accurately a

SUBMITTER: Du X 

PROVIDER: S-EPMC12464767 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature