scholarly journals Micro-Kinetic Modelling of CO-TPD from Fe(100)—Incorporating Lateral Interactions

Catalysts ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 310 ◽  
Author(s):  
Thobani Gambu ◽  
R. Abrahams ◽  
Eric van Steen
Keyword(s):  
Monte Carlo ◽  
Kinetic Modelling ◽  
Kinetic Monte Carlo ◽  
Mean Field ◽  
Temperature Programmed Desorption ◽  
Lateral Interactions ◽  
Field Techniques ◽  
Co Dissociation ◽  
The Mean ◽  
Temperature Programmed

The experimentally determined temperature programmed desorption profile of CO from Fe(100) is characterized by four maxima, i.e., α1-CO, α2-CO, α3-CO, and β-CO (see e.g., Moon et al., Surf. Sci. 1985, 163, 215). The CO-TPD profile is modeled using mean-field techniques and kinetic Monte Carlo to show the importance of lateral interactions in the appearance of the CO-TPD-profile. The inclusion of lateral interactions results in the appearance of a new maximum in the simulated CO-TPD profile if modeled using the mean-field, quasi-chemical approach or kinetic Monte Carlo. It is argued that α2-CO may thus originate from lateral interactions rather than a differently bound CO on Fe(100). A detailed sensitivity analysis of the effect of the strength of the lateral interactions between the species involved (CO, C, and O), and the choice of the transition state, which affects the activation energy for CO dissociation, and the energy barrier for diffusion on the CO-TPD profile is presented.

scholarly journals Evaluating the Benefits of Kinetic Monte Carlo and Microkinetic Modeling for Catalyst Design Studies in the Presence of Lateral Interactions

2021 ◽  
Author(s):  
Xiao Li ◽  
Lars Grabow
Keyword(s):  
Monte Carlo ◽  
Cluster Expansion ◽  
Kinetic Monte Carlo ◽  
Computational Cost ◽  
Mean Field ◽  
Catalyst Design ◽  
Lateral Interactions ◽  
Small Set ◽  
The Mean ◽  
Coverage Effects

<div><p>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.</p>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. 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.<br></div>

scholarly journals Evaluating the Benefits of Kinetic Monte Carlo and Microkinetic Modeling for Catalyst Design Studies in the Presence of Lateral Interactions

2021 ◽  
Author(s):  
Xiao Li ◽  
Lars Grabow
Keyword(s):  
Monte Carlo ◽  
Cluster Expansion ◽  
Kinetic Monte Carlo ◽  
Computational Cost ◽  
Mean Field ◽  
Catalyst Design ◽  
Lateral Interactions ◽  
Small Set ◽  
The Mean ◽  
Coverage Effects

<div><p>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.</p>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. 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.<br></div>

scholarly journals Evaluating the Benefits of Kinetic Monte Carlo and Microkinetic Modeling for Catalyst Design Studies in the Presence of Lateral Interactions

2020 ◽  
Author(s):  
Xiao Li ◽  
Lars Grabow
Keyword(s):  
Monte Carlo ◽  
Kinetic Monte Carlo ◽  
Computational Cost ◽  
Mean Field ◽  
Catalyst Design ◽  
Mean Field Approximation ◽  
Lateral Interactions ◽  
Trend Prediction ◽  
Co Oxidation Reaction ◽  
Coverage Effects

<div>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. </div><div><br></div><div>Using the prototypical CO oxidation reaction as an example, we critically evaluate the benefits of kMC over MKM in terms of trend prediction accuracy and computational cost. 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. The difference, however, is mainly manifested in the absolute rates, surface coverages and the optimal descriptor values, whereas relative activity trends remain largely intact. Moreover, the nature of the rate-determining step as identified using Campbell’s degree of rate control is also consistent between both approaches. Considering that the computational cost of MKM is ca. three orders of magnitude lower than for a kMC simulation, the MKM approach does provide the best balance between accuracy and efficiency when used in the context of computational catalyst screening.</div><div><br></div>

scholarly journals Evaluating the Benefits of Kinetic Monte Carlo and Microkinetic Modeling for Catalyst Design Studies in the Presence of Lateral Interactions

2020 ◽  
Author(s):  
Xiao Li ◽  
Lars Grabow
Keyword(s):  
Monte Carlo ◽  
Kinetic Monte Carlo ◽  
Computational Cost ◽  
Mean Field ◽  
Catalyst Design ◽  
Mean Field Approximation ◽  
Lateral Interactions ◽  
Trend Prediction ◽  
Co Oxidation Reaction ◽  
Coverage Effects

<div>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. </div><div><br></div><div>Using the prototypical CO oxidation reaction as an example, we critically evaluate the benefits of kMC over MKM in terms of trend prediction accuracy and computational cost. 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. The difference, however, is mainly manifested in the absolute rates, surface coverages and the optimal descriptor values, whereas relative activity trends remain largely intact. Moreover, the nature of the rate-determining step as identified using Campbell’s degree of rate control is also consistent between both approaches. Considering that the computational cost of MKM is ca. three orders of magnitude lower than for a kMC simulation, the MKM approach does provide the best balance between accuracy and efficiency when used in the context of computational catalyst screening.</div><div><br></div>

Kinetic Monte Carlo Simulation of Impurity Effects on Nucleation and Growth of SiC Clusters on Si(100)

2013 ◽  
Vol 740-742 ◽  
pp. 393-396
Author(s):  
Maxim N. Lubov ◽  
Jörg Pezoldt ◽  
Yuri V. Trushin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cluster Size ◽  
Kinetic Monte Carlo ◽  
Nucleation And Growth ◽  
Nucleation Density ◽  
Nucleation Process ◽  
Impurity Effects ◽  
Kinetic Monte Carlo Simulations ◽  
The Mean

The influence of attractive and repulsive impurities on the nucleation process of the SiC clusters on Si(100) surface was investigated. Kinetic Monte Carlo simulations of the SiC clusters growth show that that increase of the impurity concentration (both attractive and repulsive) leads to decrease of the mean cluster size and rise of the nucleation density of the clusters.

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