Wu Hao, Mey Antonia S J S, Rosta Edina, Noé Frank
Free University of Berlin, Arnimallee 6, 14195 Berlin, Germany.
King's College London, London, SE1 1DB, United Kingdom.
J Chem Phys. 2014 Dec 7;141(21):214106. doi: 10.1063/1.4902240.
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.
我们提出了一种基于离散转变的重加权分析方法（dTRAM），用于分析在不同热力学状态（温度、哈密顿量等）下产生的构型空间离散模拟轨迹。dTRAM能给出任意热力学状态下稳态量（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）不同，dTRAM不需要从全局平衡中采样数据，因此对于诸如并行/模拟回火、副本交换、伞形采样或元动力学等增强采样数据能够产生更优的估计。此外，dTRAM能从所有热力学状态下的离散状态空间轨迹中给出马尔可夫状态模型（MSM）的最优估计。在合适的条件下，这些MSM可用于计算动力学量（例如速率、时间尺度）。在单一热力学状态的极限情况下，dTRAM估计出最大似然可逆MSM，而在不相关采样数据的极限情况下，dTRAM与WHAM相同。因此，dTRAM是这两种估计器的推广。
