grand canonical Monte Carlo*

The structure of a surface can dramatically affect its properties. For example, surface reconstructions can occur that change band alignments and/or catalytic activity. Currently, ab initio thermodynamics is the method of choice for determining the stable surfaces of a material, however, the selection of surfaces to study is done manually, which induces bias that can prevent one from finding global minima in the surface energy. We present an implementation of ab initio grand canonical Monte Carlo (GCMC) that automatically predicts surface phase diagrams and apply it to the Ag(111) system. We obtain an Ag₇O₁₀ overlayer, which is consistent with the most stable reconstruction found experimentally and computationally. We extracted structure-stability trends from our simulation data using machine learning and find that surface coordination and bond angles are important descriptors for stability. We analyzed the stochastic evolution of the surface and discovered a possible mechanism for the formation of the Ag₇O₁₀ overlayer. Ab initio GCMC therefore offers a rich set of possibilities for studying interfacial systems.