Unveiling the catalyst deactivation mechanism in the non-oxidative dehydrogenation of light alkanes on Rh(111): Density functional theory and kinetic Monte Carlo study

Choung, Seokhyun; Kim, Yoon-Ho; Moon, Joonoh; Roh, Jangeon; Hwang, Jinwoo; Han, Jeong Woo

doi:10.1016/j.cattod.2022.06.034

Cited by 2 publications

(1 citation statement)

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“…Zacros has been used successfully to simulate a range of catalytic systems. − Still, while accounting for lateral interactions is possible, it is often extremely time-consuming, especially when the interactions are long-range. Long-range interactions are characterized by the contribution of large clusters to the CE; the cost of solving a subgraph isomorphism problem increases sharply with the number of vertices in the “query graph”, which in this case represents the cluster.…”

Section: Introductionmentioning

confidence: 99%

Speeding up the Detection of Adsorbate Lateral Interactions in Graph-Theoretical Kinetic Monte Carlo Simulations

Benson,

Yadavalli,

Stamatakis

2023

J. Phys. Chem. A

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Kinetic Monte Carlo (KMC) has become an indispensable tool in heterogeneous catalyst discovery, but realistic simulations remain computationally demanding on account of the need to capture complex and long-range lateral interactions between adsorbates. The Zacros software package (https:// zacros.org) adopts a graph-theoretical cluster expansion (CE) framework that allows such interactions to be computed with a high degree of generality and fidelity. This involves solving a series of subgraph isomorphism problems in order to identify relevant interaction patterns in the lattice. In an effort to reduce the computational burden, we have adapted two well-known subgraph isomorphism algorithms, namely, VF2 and RI, for use in KMC simulations and implemented them in Zacros. To benchmark their performance, we simulate a previously established model of catalytic NO oxidation, treating the O* lateral interactions with a series of progressively larger CEs. For CEs with long-range interactions, VF2 and RI are found to provide impressive speedups relative to simpler algorithms. RI performs best, giving speedups reaching more than 150× when combined with OpenMP parallelization. We also simulate a recently developed methane cracking model, showing that RI offers significant improvements in performance at high surface coverages.

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