Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges

Journal article

Kinetic Monte-Carlo (KMC) simulations have been instrumental in multiscale catalysis studies, enabling the elucidation of the complex dynamics of heterogeneous catalysts and the prediction of macroscopic performance metrics, such as activity and selectivity. However, the accessible length- and time-scales have been a limiting factor in such simulations. For instance, handling lattices containing millions of sites with “traditional” sequential KMC implementations is prohibitive owing to large memory requirements and long simulation times. We have recently established an approach for exact, distributed, lattice-based simulations of catalytic kinetics which couples the Time-Warp algorithm with the Graph-Theoretical KMC framework, enabling the handling of complex adsorbate lateral interactions and reaction events within large lattices. In this work, we develop a lattice-based variant of the Brusselator system, a prototype chemical oscillator pioneered by Prigogine and Lefever in the late 60’s, to benchmark and demonstrate our approach. This system can form spiral wave patterns, which would be computationally intractable with sequential KMC, while our distributed KMC approach can simulate such patterns 16 and 36 times faster with 625 and 1600 processors, respectively. The medium- and large-scale benchmarks thus conducted, demonstrate the robustness of the approach, and reveal computational bottlenecks that could be targeted in further development efforts

Authors: Pineda, M; Stamatakis, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Institute of Physics
• Journal: The Journal of Chemical Physics
• Volume: 156
• Issue: 12
• Pages: 120902-120902
• Article number: 120902
• Publication date: 2022-03-02
• DOI: 10.1063/5.0083251
• EISSN: 1089-7690
• ISSN: 0021-9606
• Language: English
• Keywords: Materials science; Physics; Kinetic Monte Carlo; Statistical physics; Mathematics; Computer science; Monte Carlo method; Bridging (networking); Nanotechnology; Range (aeronautics)