Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states.
Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states.
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DOI：
10.1063/1.4902240
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发表时间：
2014-11
期刊：
影响因子：
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通讯作者：
Hao Wu;A. Mey;E. Rosta;F. Noé
中科院分区：
文献类型：
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作者：
Hao Wu;A. Mey;E. Rosta;F. Noé
提出了一种基于离散转换的加权分析方法(DTRAM)，用于分析不同热力学状态(温度、哈密顿量等)下的组态空间离散模拟轨迹。DTRAM提供了在任何热力学状态下的固定量(概率、自由能、期望值)的最大似然估计。与加权直方图分析方法(WHAM)相比，dTRAM不需要从全局均衡中采样数据，因此可以对增强的采样数据产生更好的估计，例如并行/模拟回火、副本交换、伞形采样或元动态。此外，dTRAM从所有热力学状态下的离散化状态空间轨迹提供了马尔可夫状态模型(MSM)的最佳估计。在适当的条件下，这些MSM可用于计算运动量(例如，速率、时间尺度)。在单一热力学状态的极限下，dTRAM估计的是最大似然可逆MSM，而在不相关采样数据的极限下，dTRAM与WHAM是相同的。因此，dTRAM是对这两个估计器的推广。
We propose a discrete transition-based reweighting analysis method (dTRAM) for analyzing configuration-space-discretized simulation trajectories produced at different thermodynamic states (temperatures, Hamiltonians, etc.) dTRAM provides maximum-likelihood estimates of stationary quantities (probabilities, free energies, expectation values) at any thermodynamic state. In contrast to the weighted histogram analysis method (WHAM), dTRAM does not require data to be sampled from global equilibrium, and can thus produce superior estimates for enhanced sampling data such as parallel/simulated tempering, replica exchange, umbrella sampling, or metadynamics. In addition, dTRAM provides optimal estimates of Markov state models (MSMs) from the discretized state-space trajectories at all thermodynamic states. Under suitable conditions, these MSMs can be used to calculate kinetic quantities (e.g., rates, timescales). In the limit of a single thermodynamic state, dTRAM estimates a maximum likelihood reversible MSM, while in the limit of uncorrelated sampling data, dTRAM is identical to WHAM. dTRAM is thus a generalization to both estimators.