Accelerating and enhancing thermodynamic simulations of electrochemical interfaces
This work addresses the problem of costly and incomplete thermodynamic simulations for researchers in catalysis, energy storage, and corrosion, offering a scalable approach to material design, though it is incremental as it builds on existing methods.
The paper tackled the challenge of predicting stable surface structures at electrochemical interfaces by extending the VSSR-MC method to autonomously sample reconstructions under aqueous conditions, resulting in accurate predictions of known Pt(111) phases and new LaMnO3(001) reconstructions.
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 $\textit{ab initio}$ 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 and efficiently predict surface energetics, recovering known Pt(111) phases and revealing new LaMnO$_\mathrm{3}$(001) surface reconstructions. By explicitly accounting for bulk-electrolyte equilibria, our framework enhances electrochemical stability predictions, offering a scalable approach to understanding and designing materials for electrochemical applications.