Stochastic simulation of chemical reactions in subdiffusion medium

Zenyuk D.A.
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Theory of anomalous diffusion, which describe a vast number of transport processes with power law mean squared displacement, is actively advancing in recent years. Diffusion of liquids in porous media, carrier transport in amorphous semiconductors and molecular transport in viscous environments are widely known examples of anomalous deceleration of transport processes compared to the standard model.

Direct Monte Carlo simulation is a convenient tool for studying such processes. An efficient stochastic simulation algorithm is developed in the present paper. It is based on simple renewal process with interarrival times that have power law asymptotics. Analytical derivations show a deep connection between this class of random process and equations with fractional derivatives. The algorithm is further generalized by coupling it with chemical reaction simulation. It makes stochastic approach especially useful, because the exact form of integrodifferential evolution equations for reaction — subdiffusion systems is still a matter of debates.

Proposed algorithm relies on non-markovian random processes, hence one should carefully account for qualitatively new effects. The main question is how molecules leave the system during chemical reactions. An exact scheme which tracks all possible molecule combinations for every reaction channel is computationally infeasible because of the huge number of such combinations. It necessitates application of some simple heuristic procedures. Choosing one of these heuristics greatly affects obtained results, as illustrated by a series of numerical experiments.

Keywords: anomalous diffusion, chemical kinetics, Monte Carlo methods
Citation in English: Zenyuk D.A. Stochastic simulation of chemical reactions in subdiffusion medium // Computer Research and Modeling, 2021, vol. 13, no. 1, pp. 87-104
Citation in English: Zenyuk D.A. Stochastic simulation of chemical reactions in subdiffusion medium // Computer Research and Modeling, 2021, vol. 13, no. 1, pp. 87-104
DOI: 10.20537/2076-7633-2021-13-1-87-104
URL: http://crm-en.ics.org.ru/journal/article/3030/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

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