What's KMC All About And Why Bother

Many of us have experience in modelling heterogeneous catalysts using mean-field-type approaches, e.g. within the context of Langmuir-Hinshelwood kinetics. However, an increasing amount of research is showing that such approaches are inadequate, at least for certain systems. The approximations and simplifications employed therein can indeed lead to quantitative or even qualitative errors in the prediction of catalytic performance metrics. This tutorial is intended for readers with background on chemical kinetics but who have just started exploring kinetic Monte Carlo (KMC), and discusses what this method can do and why it is superior to mean-field-type models.

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Lattice Input for a FCC(100) Surface

One of the benefits of kinetic Monte Carlo is that it coarse-grains physical space: the reacting molecules are no longer moving and interacting in the 3D space but on a lattice. Thus, diffusion, for instance, is not captured as a continuous (x,y,z)-trajectory but rather as a transition whereby a molecule hops from one site of the lattice to another. Choosing how to map a catalytic surface onto a lattice is relatively simple, but one has to make some decisions that may be important in correctly capturing the relevant physics. This tutorial provides guidance on this matter and explains how to set up the input for a periodic lattice in Zacros. As a working example we will consider a FCC(100) surface.

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Cluster Expansion for Oxygen on Pt(111)

Adsorbate lateral interactions have recently attracted significant focus in the computational catalysis field, as they are responsible for ordering in the adsorbate overlayer, but also influence the activation energies of elementary events. Zacros treats such interactions within the general framework of cluster expansion Hamiltonians. This enables us to include long-range and many-body contributions in a simulation. Brønsted-Evans-Polanyi relations are used to correlate the activation energy of an elementary event with its reaction energy, thereby capturing the influence of neighbouring spectator adsorbates on barriers. This tutorial deals with setting up the Zacros input for a detailed cluster expansion Hamiltonian for O on Pt developed by Schneider and co-workers [J. Catal. (2012) 286: 88-94].

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Mapping DFT Energies to Zacros Input

Incorporating first-principles quantum chemistry data in kinetic Monte Carlo simulations has proven to be a powerful approach in investigating the behaviour of catalytic systems at the molecular level. Such first-principles KMC frameworks were pioneered by M. Neurock, M. Scheffler and co-workers [M. Neurock, E. W. Hansen (1998), Comp. Chem. Eng. 22: S1045-S1060; K. Reuter, D. Frenkel, M. Scheffler (2004), Phys. Rev. Lett. 93(11): 116105], and make use of the energies computed typically from density functional theory (DFT) for stable molecular species and transition states, in order to calculate rate constants. This tutorial explains how to map quantum chemistry data into energetics input in Zacros. As a working example we will consider water-gas shift pathways on Pt(111).

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Syntax Highlighting in Zacros

Zacros parses text files that follow a specific keyword-based syntax and can be generated by a text editor such as Notepad++ or Textpad. Such editors support syntax highlighting to make it easier for the user to read and modify the input. Within the Zacros distribution one can find the files that define the Zacros input language keywords for Notepad++ or Textpad. In this tutorial you will learn how to import these files and enable syntax highlighting for Zacros input files.

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