Adv Sci (Weinh). 2025 Oct 27:e13878. doi: 10.1002/advs.202513878. Online ahead of print.
ABSTRACT
Oxidizing transition metal surfaces are generally characterized by an increasing heterogeneity at simultaneous lowering of crystalline order. This complexity eludes present-day first-principles descriptions, with predictive-quality surface phase diagrams commonly derived from comparing the stability of a small number of ordered surface structural models that are motivated by partial experimental characterization or chemical intuition. Here the computational acceleration brought by machine-learned interatomic potentials is leveraged for a systematic sampling of the configurational phase space through replica exchange molecular dynamics. Thermodynamic averaging subsequently yields grand-canonical expectation values for observables like O coverage that account for the disorder and diversity of the sampled structures. Application to the initial oxidation of the Cu(111) surface reveals the (purely entropic) stabilization of sparse O adsorbates at the onset, a plethora of energetically essentially degenerate polymeric -O-Cu-O- ring and chain networks at higher O loading, as well as the presence of experimentally discussed minority species. The in silico surface phase diagram correspondingly shows marked differences to one based merely on established ordered surface reconstructions.
PMID:41144830 | DOI:10.1002/advs.202513878
