Harland Ben, Sun Sean X
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
J Chem Phys. 2007 Sep 14;127(10):104103. doi: 10.1063/1.2775439.
Markovian models based on the stochastic master equation are often encountered in single molecule dynamics, reaction networks, and nonequilibrium problems in chemistry, physics, and biology. An efficient and convenient method to simulate these systems is the kinetic Monte Carlo algorithm which generates continuous-time stochastic trajectories. We discuss an alternative simulation method based on sampling of stochastic paths. Utilizing known probabilities of stochastic paths, it is possible to apply Metropolis Monte Carlo in path space to generate a desired ensemble of stochastic paths. The method is a generalization of the path sampling idea to stochastic dynamics, and is especially suited for the analysis of rare paths which are not often produced in the standard kinetic Monte Carlo procedure. Two generic examples are presented to illustrate the methodology.
基于随机主方程的马尔可夫模型常用于单分子动力学、反应网络以及化学、物理和生物学中的非平衡问题。模拟这些系统的一种高效便捷方法是动力学蒙特卡罗算法，它能生成连续时间的随机轨迹。我们讨论一种基于随机路径采样的替代模拟方法。利用已知的随机路径概率，可以在路径空间应用 metropolis 蒙特卡罗方法来生成所需的随机路径系综。该方法是将路径采样思想推广到随机动力学，特别适用于分析标准动力学蒙特卡罗过程中不常产生的稀有路径。给出了两个一般示例来说明该方法。
