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  3. Randomized Iterative Trajectory Reweighting For Steady-state Distributions Without Discretization Error.
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  3. Randomized Iterative Trajectory Reweighting For Steady-state Distributions Without Discretization Error.

Related Experiment Video

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Randomized iterative trajectory reweighting for steady-state distributions without discretization error.

Sagar Kania1, Robert J Webber2, Gideon Simpson3

  • 1Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239.

Proceedings of the National Academy of Sciences of the United States of America
|May 6, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed the Randomized Iterative trajectory Reweighting (RiteWeight) algorithm to accurately estimate stationary distributions from molecular dynamics simulations. This method corrects flawed distributions, enabling precise calculation of molecular properties even from unconverged data.

Keywords:
Boltzmann distributionMarkov model (MSMs)molecular kineticsnonequilibrium steady statetrajectory reweighting

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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Area of Science:

  • Computational Chemistry
  • Statistical Mechanics
  • Molecular Dynamics Simulations

Background:

  • Ensuring convergence to stationary distributions in molecular dynamics (MD) simulations is crucial for accurate estimation of thermodynamic properties and transition pathways.
  • Unconverged simulations limit the reliability of calculated free energies, reaction rates, and mechanisms.

Purpose of the Study:

  • To introduce a novel algorithm, Randomized Iterative trajectory Reweighting (RiteWeight), for estimating stationary distributions from unconverged MD simulation data.
  • To address the challenge of configuration-space discretization errors in existing reweighting techniques.

Main Methods:

  • The RiteWeight algorithm iteratively reweights trajectory segments by solving for the stationary distribution of a Markov state model (MSM).
  • It employs self-consistent updates of segment weights and utilizes a novel random clustering approach in each iteration.
  • The method mitigates discretization errors, yielding quasi-continuous configuration-space distributions without requiring the Markov property at the cluster level.

    • Main Results:

    • RiteWeight accurately recovers the stationary distribution from both synthetic and atomistic MD trajectories.
    • The algorithm successfully corrects flawed distributions obtained from unconverged simulations.
    • Accurate observables for both equilibrium and nonequilibrium steady states were generated, demonstrating the method's efficacy.

    Conclusions:

    • RiteWeight provides a robust solution for estimating stationary distributions from unconverged MD data.
    • The algorithm's ability to generate accurate observables highlights the importance of correcting the underlying trajectory distribution.
    • This approach offers a valuable alternative to standard Markov State Models for analyzing molecular dynamics simulations.