论文标题
迭代轨迹重新加权，以估计平衡和非平衡观测值
Iterative trajectory reweighting for estimation of equilibrium and non-equilibrium observables
论文作者
论文摘要
我们提出了两种算法，通过这些算法，可以迭代地重新加权一组短而无偏的轨迹，以获得各种可观察物。第一个算法通过根据每个状态的平均概率迭代重新加权来估计系统的固定（稳态）分布。该算法适用于平衡或非平衡稳态，利用动态下的分布的“左”平稳性 - 即，当概率的列列乘以过渡矩阵时，在离散的设置中，在离散的设置中，将其作为左随机矩阵表达的过渡矩阵。第二个程序依赖于以行向量表示的委员会的“正确”平稳性（分裂概率）。这些算法是公正的，不依赖计算过渡矩阵，也没有对离散状态的马尔可夫假设。在这里，我们将程序应用于一维双孔电势，并将其应用于D.E.的208美元$ $ S原子TRP板折叠轨迹。肖研究。
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.
