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观点:化学动力学的随机算法。

Perspective: Stochastic algorithms for chemical kinetics.

机构信息

Dan T Gillespie Consulting, 30504 Cordoba Pl., Castaic, California 91384, USA.

出版信息

J Chem Phys. 2013 May 7;138(17):170901. doi: 10.1063/1.4801941.

Abstract

We outline our perspective on stochastic chemical kinetics, paying particular attention to numerical simulation algorithms. We first focus on dilute, well-mixed systems, whose description using ordinary differential equations has served as the basis for traditional chemical kinetics for the past 150 years. For such systems, we review the physical and mathematical rationale for a discrete-stochastic approach, and for the approximations that need to be made in order to regain the traditional continuous-deterministic description. We next take note of some of the more promising strategies for dealing stochastically with stiff systems, rare events, and sensitivity analysis. Finally, we review some recent efforts to adapt and extend the discrete-stochastic approach to systems that are not well-mixed. In that currently developing area, we focus mainly on the strategy of subdividing the system into well-mixed subvolumes, and then simulating diffusional transfers of reactant molecules between adjacent subvolumes together with chemical reactions inside the subvolumes.

摘要

我们概述了我们对随机化学动力学的看法,特别关注数值模拟算法。我们首先关注稀溶液、充分混合的系统,在过去 150 年中,使用常微分方程对其描述一直是传统化学动力学的基础。对于这样的系统,我们回顾了离散随机方法的物理和数学原理,以及为了恢复传统的连续确定性描述而需要进行的近似。接下来,我们注意到一些更有前途的策略,用于随机处理刚性系统、稀有事件和敏感性分析。最后,我们回顾了一些最近的努力,以适应和扩展离散随机方法,使其适用于不充分混合的系统。在这个正在发展的领域中,我们主要关注将系统细分为充分混合的子体积的策略,然后模拟反应物分子在相邻子体积之间的扩散转移以及子体积内的化学反应。

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