Santa Barbara High School, Santa Barbara, California 93103, USA.
J Chem Phys. 2012 Sep 7;137(9):094106. doi: 10.1063/1.4747338.
Transition path sampling (TPS) algorithms have been implemented with deterministic dynamics, with thermostatted dynamics, with Brownian dynamics, and with simple spin flip dynamics. Missing from the TPS repertoire is an implementation with kinetic Monte Carlo (kMC), i.e., with the underlying dynamics coming from a discrete master equation. We present a new hybrid kMC-TPS algorithm and prove that it satisfies detailed balance in the transition path ensemble. The new algorithm is illustrated for a simplified Markov State Model of trp-cage folding. The transition path ensemble from kMC-TPS is consistent with that obtained from brute force kMC simulations. The committor probabilities and local fluxes for the simple model are consistent with those obtained from exact methods for simple master equations. The new kMC-TPS method should be useful for analysis of rare transitions in complex master equations where the individual states cannot be enumerated and therefore where exact solutions cannot be obtained.
过渡路径采样 (TPS) 算法已经实现了确定性动力学、恒温动力学、布朗动力学和简单的自旋翻转动力学。TPS 方法中缺少的是与动力学蒙特卡罗 (kMC) 的实现，即来自离散主方程的基本动力学。我们提出了一种新的混合 kMC-TPS 算法，并证明它在过渡路径系综中满足详细平衡。该新算法通过简化的 trp-cage 折叠的马尔可夫状态模型进行了说明。从 kMC-TPS 获得的过渡路径系综与从强制 kMC 模拟获得的一致。简单模型的牵连概率和局部通量与简单主方程的精确方法获得的一致。新的 kMC-TPS 方法对于分析复杂主方程中的稀有跃迁应该是有用的，在复杂主方程中，单个状态无法枚举，因此无法获得精确解。
