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Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states | | Hao Wu
; Antonia S. J. S. Mey
; Edina Rosta
; Frank Noé
; | | Date: |
11 Nov 2014 | | Abstract: | 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. | | Source: | arXiv, 1411.2875 | | Services: | Forum | Review | PDF | Favorites | |
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