AI 中文总结
该研究采用嵌套采样模拟结合机器学习原子间势，预测Cu(100)氧化的缺失行重构，表征温度依赖的表面演化、缺陷态及有序-无序转变，为理解表面与外部条件耦合提供第一性原理级方法。
AI 中文摘要
金属表面会随化学环境发生结构、组成和形貌变化，为特定应用调控表面功能与稳定性，需理解表面演化如何与外部条件耦合。本文展示了嵌套采样模拟以第一性原理预测质量获取该耦合关系的可行性：通过探索全构型空间，嵌套采样可配分函数，并在无需先验知识的情况下直接获取任意温度下所需热力学系综平均；该方法借助机器学习原子间势、采样算法的高效GPU实现及定制采样移动，实现计算可行性。将其应用于Cu(100)的早期氧化，成功预测了实验观测到的复杂$(2\boldsymbol{\times}\boldsymbol{)}$R45°-O缺失行重构；配分函数的全量获取可详细表征温度依赖的表面演化，绘制缺陷态的出现及重构表面的有序-无序转变。
英文摘要
Metal surfaces undergo structural, compositional, and morphological changes in response to their chemical environment. Tuning the surfaces' function and stability for a given application correspondingly necessitates an understanding of how this surface evolution couples to external conditions. Here, we demonstrate the feasibility of nested sampling simulations to obtain this coupling at first-principles predictive quality. By exploring the full configuration space, nested sampling estimates the partition function and gives direct access to desired thermodynamic ensemble averages at any temperature without prior knowledge. Computational feasibility is achieved through machine-learned interatomic potentials, an efficient GPU implementation of the sampling algorithm and bespoke sampling moves. Applied to the early oxidation of Cu(100), the approach successfully predicts the experimentally observed, complex $(2\sqrt{2}\times\sqrt{2})$R45$^\circ$-O missing-row reconstruction. The full access to the partition function enables a detailed characterization of the temperature-dependent surface evolution, mapping the emergence of defect states and the order-disorder transition of the reconstructed surface.
