Synergistic Inhibition of Oxide Formation in Oxidation Catalysis: A First-Principles Kinetic Monte Carlo Study of NO + CO Oxidation at Pd(100)

On-the-fly kinetic Monte Carlo simulations with neural network potentials for surface diffusion and reaction

We develop an adaptive scheme in the kinetic Monte Carlo simulations, where the adsorption and activation energies of all elementary steps, including the effects of other adsorbates, are evaluated "on-the-fly" by employing the neural network potentials. The configurations and energies evaluated during the simulations are stored for reuse when the same configurations are sampled in a later step. The present scheme is applied to hydrogen adsorption and diffusion on the Pd(111) and Pt(111) surfaces and the CO oxidation reaction on the Pt(111) surface. The effects of interactions between adsorbates, i.e., adsorbate-adsorbate lateral interactions, are examined in detail by comparing the simulations without considering lateral interactions. This study demonstrates the importance of lateral interactions in surface diffusion and reactions and the potential of our scheme for applications in a wide variety of heterogeneous catalytic reactions.

A probabilistic microkinetic modeling framework for catalytic surface reactions

We present a probabilistic microkinetic modeling (MKM) framework that incorporates the short-ranged order (SRO) evolution for adsorbed species (adspecies) on a catalyst surface. The resulting model consists of a system of ordinary differential equations. Adsorbate-adsorbate interactions, surface diffusion, adsorption, desorption, and catalytic reaction processes are included. Assuming that the adspecies ordering/arrangement is accurately described by the SRO parameters, we employ the reverse Monte Carlo (RMC) method to extract the relevant local environment probability distributions and pass them to the MKM. The reaction kinetics is faithfully captured as accurately as the kinetic Monte Carlo (KMC) method but with a computational time requirement of few seconds on a standard desktop computer. KMC, on the other hand, can require several days for the examples discussed. The framework presented here is expected to provide the basis for wider application of the RMC-MKM approach to problems in computational catalysis, electrocatalysis, and material science.

First-principles-based kinetic Monte Carlo simulations of CO oxidation on catalytic Au(110) and Ag(110) surfaces

At the core of the development of more efficient and reliable fuel cells (FCs), there are several essential chemical reactions, namely carbon monoxide (CO) oxidation. This reaction is a keystone in the cleaning of hydrogen fuel used in fuel cells due to strong poisoning by this species of the platinum catalyst used in these devices. The present work aims to provide insight regarding the activation of CO oxidation by gold or silver microfacets possessing low coordinated atoms. To achieve this, density functional theory (DFT) quantum calculations, which determined two competing reaction pathways for CO oxidation, i.e., by molecularly adsorbed oxygen, and by dissociated oxygen, are combined with first-principles kinetic Monte Carlo (1p-kMC) simulations, which employed the resulting DFT parameters in order to address the effect of temperature and partial pressures and the interplay of the elementary reaction events. The use of 1p-kMC is a step further from available works regarding the CO oxidation on gold- and silver-based catalysts for cleansing of hydrogen that is used as a fuel in FCs. Indeed, this research contributes to the conclusion that CO oxidation should preferentially occur on silver microfacets, while the obtained turnover frequencies (TOFs) reinforced such a conclusion.

Kinetic Monte Carlo Modeling for the NO-CO Reaction Mechanism on Rh(100) and Rh(111)

The NO-CO reaction on Rh(100) and Rh(111) is a prototypical catalytic system with various practical applications, including the treatment of automotive gas exhausts. With parameters derived from first-principles calculations, the Brønsted-Evans-Polanyi (BEP) relation for the reaction steps of NO-CO on Rh(100) and Rh(111) surfaces is fitted, which is more accurate and practical for the calculation of the effect of interaction between adsorbates on activation energy compared to the basic BEP relation. Further, a kinetic Monte Carlo (kMC) model for the NO-CO reaction systems on Rh(100) and Rh(111) is constructed for the exploration of the system's reaction mechanism. Besides the temperature and pressure, the coverage and activation sites are essential factors for reaction kinetic of the NO-CO reaction system. Our results are beneficial for designing more efficient, economical, and environmentally friendly next-generation catalysts.

Theoretical insights into single-atom catalysts

Schematic diagram of theoretical models and applications of single atom catalysts. A review on the theoretical models, intrinsic properties, and the related application of SACs.

A Practical Guide to Surface Kinetic Monte Carlo Simulations

This review article is intended as a practical guide for newcomers to the field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC simulations as prevalently used for surface and interface applications. We will provide worked out examples using the kmos code, where we highlight the central approximations made in implementing a KMC model as well as possible pitfalls. This includes the mapping of the problem onto a lattice and the derivation of rate constant expressions for various elementary processes. Example KMC models will be presented within the application areas surface diffusion, crystal growth and heterogeneous catalysis, covering both transient and steady-state kinetics as well as the preparation of various initial states of the system. We highlight the sensitivity of KMC models to the elementary processes included, as well as to possible errors in the rate constants. For catalysis models in particular, a recurrent challenge is the occurrence of processes at very different timescales, e.g., fast diffusion processes and slow chemical reactions. We demonstrate how to overcome this timescale disparity problem using recently developed acceleration algorithms. Finally, we will discuss how to account for lateral interactions between the species adsorbed to the lattice, which can play an important role in all application areas covered here.

Towards operando computational modeling in heterogeneous catalysis

An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.

Assessment of mean-field microkinetic models for CO methanation on stepped metal surfaces using accelerated kinetic Monte Carlo

First-principles screening studies aimed at predicting the catalytic activity of transition metal (TM) catalysts have traditionally been based on mean-field (MF) microkinetic models, which neglect the effect of spatial correlations in the adsorbate layer. Here we critically assess the accuracy of such models for the specific case of CO methanation over stepped metals by comparing to spatially resolved kinetic Monte Carlo (kMC) simulations. We find that the typical low diffusion barriers offered by metal surfaces can be significantly increased at step sites, which results in persisting correlations in the adsorbate layer. As a consequence, MF models may overestimate the catalytic activity of TM catalysts by several orders of magnitude. The potential higher accuracy of kMC models comes at a higher computational cost, which can be especially challenging for surface reactions on metals due to a large disparity in the time scales of different processes. In order to overcome this issue, we implement and test a recently developed algorithm for achieving temporal acceleration of kMC simulations. While the algorithm overall performs quite well, we identify some challenging cases which may lead to a breakdown of acceleration algorithms and discuss possible directions for future algorithm development.

Local-metrics error-based Shepard interpolation as surrogate for highly non-linear material models in high dimensions

Many problems in computational materials science and chemistry require the evaluation of expensive functions with locally rapid changes, such as the turn-over frequency of first principles kinetic Monte Carlo models for heterogeneous catalysis. Because of the high computational cost, it is often desirable to replace the original with a surrogate model, e.g., for use in coupled multiscale simulations. The construction of surrogates becomes particularly challenging in high-dimensions. Here, we present a novel version of the modified Shepard interpolation method which can overcome the curse of dimensionality for such functions to give faithful reconstructions even from very modest numbers of function evaluations. The introduction of local metrics allows us to take advantage of the fact that, on a local scale, rapid variation often occurs only across a small number of directions. Furthermore, we use local error estimates to weigh different local approximations, which helps avoid artificial oscillations. Finally, we test our approach on a number of challenging analytic functions as well as a realistic kinetic Monte Carlo model. Our method not only outperforms existing isotropic metric Shepard methods but also state-of-the-art Gaussian process regression.

Charge Transport in Molecular Materials: An Assessment of Computational Methods

The booming field of molecular electronics has fostered a surge of computational research on electronic properties of organic molecular solids. In particular, with respect to a microscopic understanding of transport and loss mechanisms, theoretical studies assume an ever-increasing role. Owing to the tremendous diversity of organic molecular materials, a great number of computational methods have been put forward to suit every possible charge transport regime, material, and need for accuracy. With this review article we aim at providing a compendium of the available methods, their theoretical foundations, and their ranges of validity. We illustrate these through applications found in the literature. The focus is on methods available for organic molecular crystals, but mention is made wherever techniques are suitable for use in other related materials such as disordered or polymeric systems.

Surface science under reaction conditions: CO oxidation on Pt and Pd model catalysts

Application of surface-science techniques, such as XPS, SXRD, STM, and IR spectroscopy under catalytic reactions conditions yield new structural and chemical information. Recent experiments focusing on CO oxidation over Pt and Pd model catalysts were reviewed.