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This study employs nested sampling simulations to investigate the finite-temperature thermodynamics of Cu(100) oxidation, revealing how surface evolution is influenced by external conditions. By leveraging machine-learned interatomic potentials and a GPU-optimized sampling algorithm, the researchers accurately predict the complex $(2\sqrt{2}\times\sqrt{2})$R45$^\circ$-O missing-row reconstruction observed experimentally. The findings provide a comprehensive understanding of defect states and the order-disorder transition in the reconstructed surface as a function of temperature, highlighting the method's predictive power in thermodynamic modeling.
Nested sampling reveals how Cu(100) oxidation transitions through complex surface reconstructions and defect states, challenging traditional thermodynamic modeling approaches.
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.
