(732r) Modeling the Distribution of Supported Sub-Nanometer Cluster Catalysts Poster

"Supported sub-nanometer clusters are active and selective for various industrially important reactions.1 Unlike ordered catalysts, like metal/metal oxide crystals and zeolites, supported clusters present a distribution of active sites and they cannot be precisely characterized using spectroscopic methods. Furthermore, they present an intractably large configurational space, which cannot be reliably sampled using expensive ab initio computational methods. To develop a fundamental understanding of these catalysts, it is critical to predict the structural distribution of clusters under operation conditions. Here we extend our recent work from predicting the structure of monometallic clusters to bimetallic clusters.2 We find that bimetallics are inherently more complex and do not necessarily reside on a lattice but reasonable approximations could be made. We develop a computational framework to model the distribution of supported clusters using surrogate machine learning models. We apply the method to predict the distribution of PtSn clusters supported on oxide supports and predict the structure of low energy clusters and the effect of composition. Our framework can be readily applied to other supported cluster catalysts.
References
1. L. Liu, and A. Corma, Metal Catalysts for Heterogeneous Catalysis: From Single Atoms to Nanoclusters and Nanoparticles. Chem. Rev. 118(10), 4981-5079 (2018). DOI:10.1021/acs.chemrev.7b00776.
2. Y. Wang, Y.-Q. Su, E. J. M. Hensen, and D. G. Vlachos, Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization. ACS Nano 14(10), 13995-14007 (2020). DOI:10.1021/acsnano.0c06472.

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