Recent advances in descriptor-based screening methods that explicitly takes advantage of linear scaling relations between reaction and transition state energies reaction have allowed for computational design of metal alloy catalysts for a large number of industrially relevant reactions. The numerous studies have identified shortcomings of such an approach, in that the method lacks the ability to directly map catalytic properties to structural and compositional features of active sites at the atomic level. In addition, all of the current models are unable to account for morphological changes and how that affects the chemistry during operation.<br/>In some of our recent efforts, we address these <i>operando</i> gaps by developing a physics-based description that effectively connects structural and compositional features of the active sites, the energy space of metal atoms constituting the site, as well as the energy space of reaction intermediates.<br/>In this presentation, I will introduce the details of our coordination-based model, show how it extends to compositional variations, structural variations and strain and explain how the model enables prediction of relative energies of atomic arrangements for both periodic surfaces and nanoparticle models (&gt;2nm). The efficiency of the method is evident as it only requires a limited set of DFT calculations to fit the relevant parameters defining the model. The approach enables accurate and efficient modeling of how nanoparticles evolve as a function of shape, composition, and reaction environment. The presented framework provides the means to reverse engineer the morphology of realistic active site ensembles and ultimately enable inverse design of bimetallic alloy catalysts.

Developing a coordination-based model for screening of transition metal alloy nanoparticles: Applications in catalysis

ACS Spring Meeting 2021

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