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

Abstract

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.

Rhodium Single‐Atom Catalysts on Titania for Reverse Water Gas Shift Reaction Explored by First Principles Mechanistic Analysis and Compared to Nanoclusters

“…The nanoclusters may change their size and shape under reaction conditions, which is not considered in our study. The structure of a given Rh nanocluster in equilibrium with some chemical potentials of reactants could in principle be addressed using techniques such as ab initio Grand Canonical Monte Carlo, [83,89] albeit this approach is computationally demanding for nanoclusters and multicomponent reaction conditions.…”

Evolution of Metastable Structures at Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics

“…We extract raw trajectory fragments of each event with a frame rate of (0.1 ps) −1 . We first perform climbing-image nudged elastic band (CI-NEB) calculations, 19,20 using a spring constant of 5 eV/Å 2 , where the total forces, given as the sum of the spring force along the chain and the true force orthogonal to the chain, are converged to 0.1 eV/Å. If the CI-NEB fails to converge properly, the calculation is restarted with images generated from linear interpolation, using a larger spring constant.…”

Automated Detection and Characterization of Surface Restructuring Events in Bimetallic Catalysts

“…Numerous studies have employed DFT to investigate adsorbate-induced reverse segregation in Pd-doped Group 11 systems, where Pd is now stabilized on the surface by adsorbates such as CO, 5,13, O, 5,105,[110][111][112][113][114][115][116] and H. 97,102,105,108,[117][118][119][120] In Pd/Ag, for example, CO provides a stronger driving force for Pd segregation compared to H, 105 whereas O can exhibit complex coverage-dependent formation of surface oxide phases. 5,121 These observations suggest CO and O pretreatment as a promising way of activating or deactivating bimetallic catalysts. 5 Almost all of the aforementioned DFT studies employ a static approach, using flat model terraces to calculate segregation energy, defined as the change in energy as Pd goes from subsurface to surface.…”