Evaluating the benefits of kinetic Monte Carlo and microkinetic modeling for catalyst design studies in the presence of lateral interactions

Journal Article · Sat Mar 13 00:00:00 EST 2021 · Catalysis Today

DOI:https://doi.org/10.1016/j.cattod.2021.03.010· OSTI ID:1976910

Li, Xiao ^[1]; Grabow, Lars C. ^[2]

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1. University of Houston, TX (United States); OSTI
2. University of Houston, TX (United States)

Popular computational catalyst design strategies rely on the identification of reactivity descriptors, which can be used along with Brønsted–Evans–Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends. The main benefit of this approach is related to the inherent dimensionality reduction of the large material space to just a few catalyst descriptors. Conversely, it is well documented that a small set of descriptors is insufficient to capture the intricacies and complexities of a real catalytic system. The inclusion of coverage effects through lateral adsorbate-adsorbate interactions can narrow the gap between simplified descriptor predictions and real systems, but mean-field MKMs cannot properly account for local coverage effects. This shortcoming of the mean-field approximation can be rectified by switching to a lattice-based kinetic Monte Carlo (kMC) method using cluster expansion representation of adsorbate–adsorbate lateral interactions. Using the prototypical CO oxidation reaction as an example, we critically evaluate the benefits of kMC over MKM in terms of trend predictions and computational cost when using only a small set of input parameters. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. Here, the mean-field implementation applies coverage corrections directly to the descriptors, causing an artificial overprediction of the activity of strongly binding metals. In contrast, the cluster expansion in kMC implementation can differentiate among the highly active metals but it is very sensitive to the set of included interaction parameters. Considering that computational screening relies on a minimal set of descriptors, for which MKM makes reasonable trend predictions at a ca. three orders of magnitude lower computational cost than kMC, the MKM approach does provide a better entry point for computational catalyst design.

• Research Organization: Univ. of California, Oakland, CA (United States); University of Houston, TX (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
• Grant/Contract Number: AC02-05CH11231; SC0011983
• OSTI ID: 1976910
• Alternate ID(s): OSTI ID: 1843863; OSTI ID: 1984391
• Journal Information: Catalysis Today, Journal Name: Catalysis Today Journal Issue: C Vol. 387; ISSN 0920-5861
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
97 MATHEMATICS AND COMPUTING
CO oxidation
Catalyst design
Coverage effects
Kinetic Monte Carlo
Microkinetic modeling
catalyst design
coverage effects
kinetic Monte Carlo
microkinetic modeling