Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states
- Free University of Berlin, Arnimallee 6, 14195 Berlin (Germany)
- King's College London, London, SE1 1DB (United Kingdom)
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.
- OSTI ID:
- 22423764
- Journal Information:
- Journal of Chemical Physics, Vol. 141, Issue 21; Other Information: (c) 2014 Author(s); Country of input: International Atomic Energy Agency (IAEA); ISSN 0021-9606
- Country of Publication:
- United States
- Language:
- English
Similar Records
Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias
Molecular latent space simulators