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Active Site Representation in First-Principles Microkinetic Models: Data-Enhanced Computational Screening for Improved Methanation Catalysts

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Reuter,  Karsten
Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universitat Munchen;
Theory, Fritz Haber Institute, Max Planck Society;

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Citation

Deimel, M., Reuter, K., & Andersen, M. (2020). Active Site Representation in First-Principles Microkinetic Models: Data-Enhanced Computational Screening for Improved Methanation Catalysts. ACS Catalysis, 10(22), 13729-13736. doi:10.1021/acscatal.0c04045.


Cite as: http://hdl.handle.net/21.11116/0000-0007-877E-4
Abstract
Computational screening based on first-principles microkinetic modeling has evolved into a widespread tool for catalyst discovery. Efficiently exploiting various scaling relations, this approach draws its predictive character from reliable adsorption energies, typically calculated with density-functional theory (DFT). In prevalent screening approaches, the concomitant computational costs are kept tractable through the use of reductionist microkinetic models that only resolve a minimalistic amount of active site motifs at the catalyst surface. Here, we scrutinize this common practice by systematically comparing the screening predictions for the CO methanation reaction when using microkinetic models that resolve an increasing amount of sites, up to the full consideration of all high-symmetry sites at stepped transition metal (TM) and binary TM alloy catalysts. Apart from generally overestimating the catalytic activity, the simplified models fail to identify a most promising class of layered bimetallic alloys as their insufficient representation of the catalyst surface does not allow them to correctly capture the rate-determining step. Only the full microkinetic model provides this proper mechanistic basis for the screening. The excessive amount of predictive-quality adsorption energetics required for this model is obtained from a compressed sensing descriptor that once trained readily provides these data for a new material from a single DFT calculation of the clean surface. With the current methodological advances in areas such as compressed sensing and machine learning, and the concurrent availability of cheap adsorption energetics for a wide range of possible catalyst materials, there is thus no reason to continue to use simplistic microkinetic models in computational catalyst screening