Speeding up the Detection of Adsorbate Lateral Interactions in Graph-Theoretical Kinetic Monte Carlo Simulations.

Kinetic Monte Carlo (KMC) has become an indispensable tool in heterogeneous catalyst discovery, but realistic simulations remain computationally demanding on account of the need to capture complex and long-range lateral interactions between adsorbates. The Zacros software package (https://zacros.org) adopts a graph-theoretical cluster expansion (CE) framework that allows such interactions to be computed with a high degree of generality and fidelity. This involves solving a series of subgraph isomorphism problems in order to identify relevant interaction patterns in the lattice. In an effort to reduce the computational burden, we have adapted two well-known subgraph isomorphism algorithms, namely, VF2 and RI, for use in KMC simulations and implemented them in Zacros. To benchmark their performance, we simulate a previously established model of catalytic NO oxidation, treating the O* lateral interactions with a series of progressively larger CEs. For CEs with long-range interactions, VF2 and RI are found to provide impressive speedups relative to simpler algorithms. RI performs best, giving speedups reaching more than 150× when combined with OpenMP parallelization. We also simulate a recently developed methane cracking model, showing that RI offers significant improvements in performance at high surface coverages.

Assessing the Impact of Adlayer Description Fidelity on Theoretical Predictions of Coking on Ni(111) at Steam Reforming Conditions.

Methane steam reforming is an important industrial process for hydrogen production, employing Ni as a low-cost, highly active catalyst, which, however, suffers from coking due to methane cracking. Coking is the accumulation of a stable poison over time, occurring at high temperatures; thus, to a first approximation, it can be treated as a thermodynamic problem. In this work, we developed an Ab initio kinetic Monte Carlo (KMC) model for methane cracking on Ni(111) at steam reforming conditions. The model captures C-H activation kinetics in detail, while graphene sheet formation is described at the level of thermodynamics, to obtain insights into the "terminal (poisoned) state" of graphene/coke within reasonable computational times. We used cluster expansions (CEs) of progressively higher fidelity to systematically assess the influence of effective cluster interactions between adsorbed or covalently bonded C and CH species on the "terminal state" morphology. Moreover, we compared the predictions of KMC models incorporating these CEs into mean-field microkinetic models in a consistent manner. The models show that the "terminal state" changes significantly with the level of fidelity of the CEs. Furthermore, high-fidelity simulations predict C-CH island/rings that are largely disconnected at low temperatures but completely encapsulate the Ni(111) surface at high temperatures.

DFT benchmark studies on representative species and poisons of methane steam reforming on Ni(111).

Ni catalysts used in methane steam reforming (MSR) are highly susceptible to poisoning by carbon-based species, which poses a major impediment to the productivity of industrial operations. These species encompass graphitic carbon-like formations that are typically modelled as graphene. First principles-based approaches, such as density functional theory (DFT) calculations, can provide valuable insight into the mechanism of graphene growth in the MSR reaction. It is, however, critical that a DFT model of this reaction can accurately describe the interactions of Ni(111) with the MSR intermediates as well as graphene. In this work, a systematic benchmark study has been carried out to identify a suitable DFT functional for the graphene-MSR system. The binding energies of graphene and important MSR species, as well as the reaction energies of methane dissociation and carbon oxidation, were computed on Ni(111) using GGA functionals, DFT-D and van der Waals density functionals (vdW-DF). It is well-established that the GGA functionals are inadequate for describing graphene-Ni(111) interactions. In the case of vdW-DF, the optPBE-vdW functional predicts the binding energies of graphene and several important MSR species with reasonable accuracy; however, it provides poor estimates of CO and O binding energies. Among the DFT-D functionals, PBE-D3 and PBE-dDsC have been found to exhibit acceptable accuracy for graphene and most MSR species (excluding adsorbed CO), and therefore, both functionals are promising for elucidating carbon-based catalytic poisoning in the MSR reaction. Overall, no single DFT functional could estimate the binding energies of all the species with equally high accuracy.