Density functional theory (DFT) is widely used to predict the structures and properties of materials, but its direct applications to nanomaterials of experimentally relevant sizes can be prohibitively expensive. It has been demonstrated that this problem can be addressed through the generation of cluster expansion models trained by DFT. Cluster expansions are generalized Ising models that account for many-body interactions, and they are capable of accurately (within 5 meV / atom compared to DFT) predicting the energies of millions of structures per minute. In this talk, two examples of using cluster expansions to better understand structure-property relationships in nanomaterials are given. In the first example, we compare predicted Pt-Cu nanoparticle structures with experimental characterization. We demonstrate that the best agreement is achieved by constructing a novel cluster expansion for alloy nanoparticles of varying shape and size that explicitly includes adsorbates, enabling the prediction of nanoparticle structure in an oxidizing environment. In the second example we present a study of the hydrogen evolution reaction (HER) on transition metal phosphide surfaces. Cluster expansions are used to predict structures and energetics of adsorbed hydrogen as a function of temperature and applied potential, allowing us to determine the potential-dependent activities of different sites while fully accounting for interactions among adsorbed hydrogen atoms.

Understanding structure-property relationships in catalysts using cluster expansions

2019 Spring National Meeting

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