用机器学习加速电化学界面模拟，预测表面重构与稳定性。
Accelerating and enhancing thermodynamic simulations of electrochemical interfaces
- 结合机器学习力场与蒙特卡洛采样，自动搜索表面重构路径。
- 成功复现铂(111)已知相，发现氧化物新表面结构。
- 适合材料设计、电催化与电池研究者参考。
电化学界面在催化、储能和腐蚀中至关重要，其稳定性和反应性取决于电极、吸附物与电解质间的复杂相互作用。传统表面普尔巴伊图依赖专家经验或高成本的第一性原理采样，且忽略与环境的热力学平衡。机器学习势能可加速静态建模，但常忽视动态表面演化。本文扩展虚拟表面位点弛豫-蒙特卡洛方法（VSSR-MC），在水溶液电化学条件下自主采样表面重构。通过微调基础机器学习力场，准确高效预测表面能量，恢复已知的Pt(111)相，并揭示LaMnO₃(001)的新表面重构。通过显式考虑体相-电解质平衡，该框架提升电化学稳定性预测能力，为理解与设计电化学材料提供可扩展方法。
原文摘要 · Abstract (English)
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.
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