论文标题
与MBAR和TRAM的随机近似：批次自由能估计
Stochastic approximation to MBAR and TRAM: batch-wise free energy estimation
论文作者
论文摘要
分子的动力学受长寿命（亚稳态）状态之间罕见的事件过渡的控制。为了有效地探索这些过渡，已经引入了许多增强的采样方案，其中涉及使用具有偏见或温度变化的模拟。从此类模拟获得的两个建立的统计上最佳估计量是获得该模拟的无偏平衡特性的，是多态Bennett接受率（MBAR）和基于过渡的重新加权分析方法（TRAM）。 MBAR和TRAM都迭代解决，可能会遭受较长的收敛时间。在这里，我们引入了两个估计量的随机近似器（SA），导致Sambar和Satram，这些估计值显示出比其确定性同行更快的收敛速度，而没有明显的准确性损失。两种方法都在不同的分子系统上证明。
The dynamics of molecules are governed by rare event transitions between long-lived (metastable) states. To explore these transitions efficiently, many enhanced sampling protocols have been introduced that involve using simulations with biases or changed temperatures. Two established statistically optimal estimators for obtaining unbiased equilibrium properties from such simulations are the multistate Bennett Acceptance Ratio (MBAR) and the transition-based reweighting analysis method (TRAM). Both MBAR and TRAM are solved iteratively and can suffer from long convergence times. Here we introduce stochastic approximators (SA) for both estimators, resulting in SAMBAR and SATRAM, which are shown to converge faster than their deterministic counterparts, without significant accuracy loss. Both methods are demonstrated on different molecular systems.
