Parallel kinetic Monte Carlo simulation framework incorporating accurate models of adsorbate lateral interactions

Nielsen, Jens Hedegaard; d’Avezac, Mayeul; Hetherington, James; Stamatakis, Michail

doi:10.1063/1.4840395

Cited by 143 publications

(283 citation statements)

References 73 publications

(101 reference statements)

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“…We perform simulations within the graph-theoretical KMC framework as implemented in Zacros, version 1.02 [43][44][45]. We ramp the simulation temperature at a rate of 1 K s −1 to simulate TPD.…”

Section: Kinetic Monte Carlo Setupmentioning

confidence: 99%

Carbon Monoxide Poisoning Resistance and Structural Stability of Single Atom Alloys

et al. 2018

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Platinum group metals (PGMs) serve as highly active catalysts in a variety of heterogeneous chemical processes. Unfortunately, their high activity is accompanied by a high affinity for CO and thus, PGMs are susceptible to poisoning. Alloying PGMs with metals exhibiting lower affinity to CO could be an effective strategy toward preventing such poisoning. In this work, we use density functional theory to demonstrate this strategy, focusing on highly dilute alloys of PGMs (Pd, Pt, Rh, Ir and Ni) with poison resistant coinage metal hosts (Cu, Ag, Au), such that individual PGM atoms are dispersed at the atomic limit forming single atom alloys (SAAs). We show that compared to the pure metals, CO exhibits lower binding strength on the majority of SAAs studied, and we use kinetic Monte Carlo simulation to obtain relevant temperature programed desorption spectra, which are found to be in good agreement with experiments. Additionally, we consider the effects of CO adsorption on the structure of SAAs. We calculate segregation energies which are indicative of the stability of dopant atoms in the bulk compared to the surface layer, as well as aggregation energies to determine the stability of isolated surface dopant atoms compared to dimer and trimer configurations. Our calculations reveal that CO adsorption induces dopant atom segregation into the surface layer for all SAAs considered here, whereas aggregation and island formation may be promoted or inhibited depending on alloy constitution and CO coverage. This observation suggests the possibility of controlling ensemble effects in novel catalyst architectures through CO-induced aggregation and kinetic trapping.

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Section: Kinetic Monte Carlo Setupmentioning

confidence: 99%

Carbon Monoxide Poisoning Resistance and Structural Stability of Single Atom Alloys

et al. 2018

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“…kMC has proven its potential in this respect having been successfully applied to a large variety of catalytic systems. [24][25][26][27][28][29][30][31][32][33][34][35][36][37] A recent kMC framework is the graph-theoretical Zacros code of Stamatakis et al 38,39 which is particularly suited for applications in catalysis, since it is able to deal with multidentate species, complex elementary steps (e.g. involving more than two sites in specific geometric arrangements), and reaction barriers changing in the presence of spectator species that exert lateral interactions.…”

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confidence: 99%

A machine learning approach to graph-theoretical cluster expansions of the energy of adsorbate layers

Vignola

Steinmann

Vandegehuchte

et al. 2017

The Journal of Chemical Physics

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_________________________________________The accurate description of the energy of adsorbate layers is crucial for the understanding of chemistry at interfaces. For heterogeneous catalysis not only the interaction of the adsorbate with the surface, but also adsorbate-adsorbate lateral interactions significantly affect activation energies of reactions. Modeling the interactions of adsorbates with the catalyst surface and with each other can be efficiently achieved in the cluster expansion Hamiltonian formalism, which has recently been implemented in a graph-theoretical kinetic Monte Carlo (kMC) scheme to describe multi-dentate species. Automating the development of the cluster expansion Hamiltonians for catalytic systems is challenging and requires the mapping of adsorbate configurations for extended adsorbates onto a graphical lattice. The current work adopts machine learning methods to reach this goal. Clusters are automatically detected based on formalized, but intuitive chemical concepts. The corresponding energy coefficients for the cluster expansion are calculated by an inversion scheme. The potential of this method is demonstrated for the example of ethylene adsorption on Pd (111), for which we propose several expansions, depending on the graphical lattice. It turns out that for this system the best description is obtained as a combination of single molecule patterns and a few coupling terms accounting for lateral interactions. _______________________________________

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“…Such models have long been used to study the thermodynamics of adsorption, adlayer ordering, phase change, and reconstruction on surfaces [74]. It is only recently that they have been incorporated into kinetic models [75][76][77], and have already shown remarkable potential in elucidating subtle features in catalytic systems, such as the inhomogeneity in the chemical reactivity of even structurally simple single-crystal surfaces [75].…”

Section: 222mentioning

confidence: 99%

“…A parametrization that relates these constants with the activation and reaction energies in the zero-coverage limit (E ‡ f wd,0 or E ‡ rev,0 , and E rxn,0 respectively) as well as the 'proximity factor' ω of the reaction (a parameter quantifying how reactant-or product-like the transition state is [88]) has been recently implemented in KMC by Nielsen et al [76]: (16) or (17) ensure thermodynamic consistency in a KMC simulation [89] and allow for a quantitative (or semi-quantitative) treatment of the effect of lateral interactions on the reaction rate. The predictions of the barriers from these equations are of course limited by how well the linear functions fit the first-principles data, and recent studies that have implemented this methodology show errors of about 0.2 eV at most [75,77] (which is within the acceptable DFT error range of a few tens of an eV [90,91]).…”

Section: Modelling Kinetics Of Elementary Eventsmentioning

confidence: 99%

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Kinetic modelling of heterogeneous catalytic systems

Stamatakis

2014

J. Phys.: Condens. Matter

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The importance of heterogeneous catalysis in modern life is evidenced by the fact that numerous products and technologies routinely used nowadays involve catalysts in their synthesis or function. The discovery of catalytic materials is, however, a non-trivial procedure, requiring tedious trial-and-error experimentation. First-principles-based kinetic modelling methods have recently emerged as a promising way to understand catalytic function and aid in materials discovery. In particular, kinetic Monte Carlo (KMC) simulation is increasingly becoming more popular, as it can integrate several sources of complexity encountered in catalytic systems, and has already been used to successfully unravel the underlying physics of several systems of interest. After a short discussion of the different scales involved in catalysis, we summarize the theory behind KMC simulation, and present the latest KMC computational implementations in the field. Early achievements that transformed the way we think about catalysts are subsequently reviewed in connection to latest studies of realistic systems, in an attempt to highlight how the field has evolved over the last few decades. Present challenges and future directions and opportunities in computational catalysis are finally discussed.

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Parallel kinetic Monte Carlo simulation framework incorporating accurate models of adsorbate lateral interactions

Cited by 143 publications

References 73 publications

Carbon Monoxide Poisoning Resistance and Structural Stability of Single Atom Alloys

Carbon Monoxide Poisoning Resistance and Structural Stability of Single Atom Alloys

A machine learning approach to graph-theoretical cluster expansions of the energy of adsorbate layers

Kinetic modelling of heterogeneous catalytic systems

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