Microscopic reweighting for non-equilibrium steady states dynamics
arXiv:1907.08480 · doi:10.1103/PhysRevE.100.060103
Abstract
Computer simulations generate trajectories at a single, well-defined thermodynamic state point. Statistical reweighting offers the means to reweight static and dynamical properties to different equilibrium state points by means of analytic relations. We extend these ideas to non-equilibrium steady states by relying on a maximum path entropy formalism subject to physical constraints. Stochastic thermodynamics analytically relates the forward and backward probabilities of any pathway through the external non-conservative force, enabling reweighting both in and out of equilibrium. We avoid the combinatorial explosion of microtrajectories by systematically constructing pathways through Markovian transitions. We further identify a quantity that is invariant to dynamical reweighting, analogous to the density of states in equilibrium reweighting.
References in corpus (12)
- Entropy production along a stochastic trajectory and an integral fluctuation theorem
- Stochastic thermodynamics: Principles and perspectives
- Probability currents as principal characteristics in the statistical mechanics of non-equilibrium steady states
- Maximum Caliber: a general variational principle for dynamical systems
- Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states
- On the second fluctuation--dissipation theorem for nonequilibrium baths
- Girsanov reweighting for path ensembles and Markov state models
- Consistent interpretation of molecular simulation kinetics using Markov state models biased with external information
- A derivation of the master equation from path entropy maximization
- On the relaxation to nonequilibrium steady states
- Absence of dissipation in trajectory ensembles biased by currents
- Minimal constraints for Maximum Caliber analysis of dissipative steady state systems