Stamatakis Michail, Vlachos Dionisios G
Department of Chemical Engineering, University of Delaware, Newark, DE 19716, USA.
Comput Chem Eng. 2011 Dec 14;35(12):2602-2610. doi: 10.1016/j.compchemeng.2011.05.008.
Well-mixed and lattice-based descriptions of stochastic chemical kinetics have been extensively used in the literature. Realizations of the corresponding stochastic processes are obtained by the Gillespie stochastic simulation algorithm and lattice kinetic Monte Carlo algorithms, respectively. However, the two frameworks have remained disconnected. We show the equivalence of these frameworks whereby the stochastic lattice kinetics reduces to effective well-mixed kinetics in the limit of fast diffusion. In the latter, the lattice structure appears implicitly, as the lumped rate of bimolecular reactions depends on the number of neighbors of a site on the lattice. Moreover, we propose a mapping between the stochastic propensities and the deterministic rates of the well-mixed vessel and lattice dynamics that illustrates the hierarchy of models and the key parameters that enable model reduction.
随机化学动力学的充分混合和基于格点的描述在文献中已被广泛使用。相应随机过程的实现分别通过 Gillespie 随机模拟算法和格点动力学蒙特卡罗算法获得。然而,这两个框架一直没有联系起来。我们展示了这些框架的等价性,即随机格点动力学在快速扩散极限下简化为有效的充分混合动力学。在后者中,格点结构隐含出现,因为双分子反应的总反应速率取决于格点上一个位点的邻居数量。此外,我们提出了随机倾向与充分混合容器和格点动力学的确定性速率之间的映射,这说明了模型的层次结构以及实现模型简化的关键参数。