Path probability ratios for Langevin dynamics -- exact and approximate

Abstract

Path reweighting is a principally exact method to estimate dynamic properties from biased simulations - provided that the path probability ratio matches the stochastic integrator used in the simulation. Previously reported path probability ratios match the Euler-Maruyama scheme for overdamped Langevin dynamics. Since MD simulations use Langevin dynamics rather than overdamped Langevin dynamics, this severely impedes the application of path reweighting methods. Here, we derive the path probability ratio $M_L$ for Langevin dynamics propagated by a variant of the Langevin Leapfrog integrator. This new path probability ratio allows for exact reweighting of Langevin dynamics propagated by this integrator. We also show that a previously derived approximate path probability ratio $M_{\mathrm{approx}}$ differs from the exact $M_L$ only by $\mathcal{O}({\xi }^4\Delta t^4)$, and thus yields highly accurate dynamic reweighting results. ($\Delta t$ is the integration time step, $\xi$ is the collision rate.) The results are tested and the efficiency of path-reweighting is explored using butane as an example.

References in corpus (6)

• Accurate sampling using Langevin dynamics
• Maximum Caliber: a general variational principle for dynamical systems
• Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states
• Stochastic actions for diffusive dynamics: Reweighting, sampling, and minimization
• xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states
• Path sampling with Stochastic Dynamics: Some new Algorithms