Sakuraba Shun, Kitao Akio
Graduate School of Frontier Sciences, The University of Tokyo, Japan.
J Comput Chem. 2009 Sep;30(12):1850-8. doi: 10.1002/jcc.21186.
We herein propose the multiple Markov transition matrix method (MMMM), an algorithm by which to estimate the stationary probability distribution from independent multiple molecular dynamics simulations with different Hamiltonians. Applications to the potential of mean force calculation in combination with the umbrella sampling method are presented. First, the performance of the MMMM is examined in the case of butane. Compared with the weighted histogram analysis method (WHAM), the MMMM has an advantage with respect to the reasonable evaluation of the stationary probability distribution even from nonequilibrium trajectories. This method is then applied to Met-enkephalin nonequilibrium simulation.
我们在此提出多重马尔可夫转移矩阵方法（MMMM），这是一种通过对具有不同哈密顿量的独立多重分子动力学模拟来估计稳态概率分布的算法。本文展示了该方法与伞形抽样法相结合在平均力势计算中的应用。首先，在丁烷的案例中检验了MMMM的性能。与加权直方图分析方法（WHAM）相比，即使从非平衡轨迹出发，MMMM在合理评估稳态概率分布方面具有优势。然后将该方法应用于甲硫氨酸脑啡肽的非平衡模拟。
