平衡和非平衡观测量估计的迭代轨迹加权法
Iterative trajectory reweighting for estimation of equilibrium and non-equilibrium observables
摘要：
我们提出了两种算法，一组短的，无偏的轨迹可以迭代地重新加权，以获得各种可观的。第一个算法估计的平稳（稳态）分布的系统迭代重新加权的轨迹的基础上，在每个状态的平均概率。该算法适用于平衡或非平衡稳态，利用动态下分布的“左”平稳性-即，在离散设置中，概率的列向量乘以表示为左随机矩阵的转移矩阵。第二个过程依赖于提交者的“右”稳定性（分裂概率），表示为行向量。该算法是无偏的，不依赖于计算转移矩阵，并使离散状态的马尔可夫假设。在这里，我们应用程序的一维双势阱，并到208 $μ$的原子Trp笼折叠轨迹从D. E。Shaw Research.
英文摘要：
We present two algorithms by which a set of short, unbiased trajectories can be iteratively reweighted to obtain various observables. The first algorithm estimates the stationary (steady state) distribution of a system by iteratively reweighting the trajectories based on the average probability in each state. The algorithm applies to equilibrium or non-equilibrium steady states, exploiting the `left' stationarity of the distribution under dynamics -- i.e., in a discrete setting, when the column vector of probabilities is multiplied by the transition matrix expressed as a left stochastic matrix. The second procedure relies on the `right' stationarity of the committor (splitting probability) expressed as a row vector. The algorithms are unbiased, do not rely on computing transition matrices, and make no Markov assumption about discretized states. Here, we apply the procedures to a one-dimensional double-well potential, and to a 208$μ$s atomistic Trp-cage folding trajectory from D.E. Shaw Research.
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