(427e) A Graph Theory-Based Approach to Determine Unique Configurations of Multidentate Adsorbates and High Adsorbate Coverages for Heterogeneous Catalysis

Heterogeneous catalysts form a crucial component of various vital processes, ranging from ammonia synthesis to cleanup of exhaust gases, and therefore are an important part of our society. To gain an understanding of the atomic scale phenomenon for these catalytic systems, ab-initio Density Functional Theory (DFT) is a widely used tool, and it has been successfully used to model many catalytic systems of varying degrees of complexity. Recently, growing computational power has begun to enable the extension of DFT analyses to understand complex reaction networks involving high adsorbate coverages, multidentate adsorbates, or combinations thereof. Although promising results have emerged, the vast combinatorial space implies that large numbers of explicit simulations are required to treat such systems, and these studies can therefore benefit from a more algorithmic approach.

To systematically account for the large and complex sample spaces described above, we present a generalized Python-based graph theory approach to convert atomic scale models into undirected graph representations. Next, we describe general graph-based algorithms for detecting unique adsorption sites as well as adsorbate configurations, and we illustrate the use of these algorithms for two model catalytic systems. First, the application of these algorithms to construct a high coverage phase diagram of intermediate NO on low symmetry Pt-Sn alloy surfaces, incorporating an evolutionary algorithm to effectively sample and reduce the total number of simulations required to find most stable high coverage NO configurations, is presented. Second, graph networks are utilized to find unique configurations of multi-dentate intermediates relevant to propane dehydrogenation reaction on alloy catalyst. We close by highlighting the future application of these graph-based frameworks as versatile and efficient means to encode complex atomic scale data, and we briefly discuss their potential application in data mining and machine learning useful information in the area of heterogeneous catalysis.