Automatic Prediction of Surface Phase Diagrams Using Ab Initio 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 (GCMC) 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.