Generalized Temporal Acceleration Scheme for Kinetic Monte Carlo Simulations of Surface Catalytic Processes by Scaling the Rates of Fast Reactions

Abstract

A novel algorithm is presented that achieves temporal acceleration during kinetic Monte Carlo (KMC) simulations of surface catalytic processes. This algorithm allows for the direct simulation of reaction networks containing kinetic processes occurring on vastly disparate time scales which computationally overburden standard KMC methods. Previously developed methods for temporal acceleration in KMC were designed for specific systems and often require a priori information from the user such as identifying the fast and slow processes. In the approach presented herein, quasi-equilibrated processes are identified automatically based on previous executions of the forward and reverse reactions. Temporal acceleration is achieved by automatically scaling the intrinsic rate constants of the quasi-equilibrated processes, bringing their rates closer to the time scales of the slow kinetically relevant nonequilibrated processes. All reactions are still simulated directly, although with modified rate constants. Abrupt changes in the underlying dynamics of the reaction network are identified during the simulation, and the reaction rate constants are rescaled accordingly. The algorithm was utilized here to model the Fischer-Tropsch synthesis reaction over ruthenium nanoparticles. This reaction network has multiple time-scale-disparate processes which would be intractable to simulate without the aid of temporal acceleration. The accelerated simulations are found to give reaction rates and selectivities indistinguishable from those calculated by an equivalent mean-field kinetic model. The computational savings of the algorithm can span many orders of magnitude in realistic systems, and the computational cost is not limited by the magnitude of the time scale disparity in the system processes. Furthermore, the algorithm has been designed in a generic fashion and can easily be applied to other surface catalytic processes of interest.

General concepts, assumptions, drawbacks, and misuses in kinetic Monte Carlo and microkinetic modeling simulations applied to computational heterogeneous catalysis

“…However, the fact that AS‐KMC identifies processes based on the configuration of the entire system is likely to make it not efficient enough for complex reaction models such as, WGSR or Fischer–Tropsch synthesis where an enormous number of possible configurations needs to be considered. This latter problem was addressed in the recently developed algorithm by Dybeck et al, where the acceleration is accomplished by reducing the reaction rates of the fast‐quasi‐equilibrated processes to enable more frequent execution of the slower reactive surface processes. The main improvement is that the partitioning and the scaling is applied to all of the processes in a given reaction channel rather than to the individual processes as done in the Voter scheme.…”

“…Typically, every kMC simulation will involve a huge number of steps (i.e., 10 8 –10 11 ) until the system achieves a steady‐state [i.e., temporal convergence of coverages (θ) and turnover frequencies (TOF), sometimes also called turnover rates (TOR)]. Temporal acceleration of kMC simulations to overcome the problem of the large differences in the time scales of surface processes can be carried out using more refined algorithms . ] Additional simple techniques can also be considered to reduce the kMC computational cost, as for instance, the use of scaling factors in reaction rates for the very fast processes (e.g., diffusion rates) or beginning the kMC simulation from a lattice with an initial coverage obtained from a MM solution.…”

Mechanisms and Dynamics of Mineral Dissolution: A New Kinetic Monte Carlo Model

“…There are several algorithms to avoid such issues, [38][39][40] yet a new one was used based on Poisson processes statistics, explained in detail in Supporting Information. These fast events happen continuously, inducing an exponential increase on the use of computing resources, blocking the simulation.…”

MonteCoffee: A programmable kinetic Monte Carlo framework

“…The task of accelerating simulations is relatively simply achieved in MonteCoffee as it uses custom functions to get rateconstants and perform the events. Two acceleration schemes are available in the code: Slowing down fast events by raising energy barriers manually and the generalized temporal acceleration scheme of Dybeck et al 18 The generalized temporal acceleration scheme involves artificially slowing down quasiequilibrated reaction channels to increase the time step of the simulations.…”

“…20 Similar approaches have been applied and justified previously. [15][16][17][18]20,28,29 For each reactive event, one needs to define the function possible(system,i site,i other) that returns True if the event presently is possible, the function get rate(system,i site,i other) that returns the rate constant, and the function do event(system, i site,i other) that modifies the occupation according to the reaction. To enhance readability, we will not repeat the input arguments to these functions in the following discussion.…”