Automatic Prediction of Surface Phase Diagrams Using Ab lnitio Grand Canonical Monte Carlo

The properties of a material are often strongly influenced by its surfaces. Depending on the nature of the chemical bonding in a material, its surface can undergo a variety of stabilizing reconstructions that dramatically alter the chemical reactivity, light absorption, and electronic band offsets. For decades, ab initio thermodynamics has been the method of choice for computationally determining the surface phase diagram of a material under different conditions. The surfaces considered for these studies, however, are often human-selected and too few in number, leading both to insufficient exploration of all possible surfaces and to biases toward portions of the composition structure phase space that often do not encompass the most stable surfaces. To overcome these limitations and automate the discovery of realistic surfaces, we combine density functional theory and grand canonical Monte Carlo (GCMG) into ab initio GCMC. This paper presents the successful application of ab initio GCMC to the study of oxide overlayers on Ag(111), which, for many years, mystified experts in surface science and catalysis. Specifically, we report that ab initio GCMC is able to reproduce the surface phase diagram of Ag(111) with no preconceived notions about the system. Using nonlinear, random forest regression, we discover that the Ag coordination number with O and the surface O-Ag-O bond angles are good descriptors of the surface energy. Additionally, using the composition structure evolution histories produced by ab initio GCMC, we deduce a mechanism for the formation of oxide overlayers based on the Ag3O4 pyramid motif that is common to many reconstructions of Ag(111). In conclusion, ab initio GCMC is a promising tool for the discovery of realistic surfaces that can then be used to study phenomena on complex surfaces such as heterogeneous catalysis and material growth, enabling reliable and insightful interpretations of experiments.