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A Minimal, Adaptive Binning Scheme for Weighted Ensemble Simulations.

Paul A Torrillo1, Anthony T Bogetti1, Lillian T Chong1

  • 1Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States.

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A new minimal adaptive binning (MAB) scheme automates bin placement for weighted ensemble path sampling. This method efficiently simulates rare events, improving pathway diversity and rate constant estimation over large free energy barriers.

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Area of Science:

  • Computational Chemistry
  • Statistical Mechanics
  • Molecular Dynamics

Background:

  • Weighted ensemble path sampling is a powerful technique for simulating rare kinetic events.
  • A key challenge is the effective division of configurational space into sampling bins.
  • Manual or fixed binning can be inefficient for complex systems.

Purpose of the Study:

  • To introduce a minimal adaptive binning (MAB) scheme for automated bin placement.
  • To enhance the efficiency of weighted ensemble path sampling.
  • To improve the simulation of rare events and sampling of molecular conformations.

Main Methods:

  • Development of the minimal adaptive binning (MAB) scheme.
  • Integration of MAB within the weighted ensemble path sampling framework.
  • Comparison of MAB with manual, fixed binning strategies.

Main Results:

  • The MAB scheme automates bin placement along a progress coordinate.
  • MAB demonstrated superior efficiency in generating transitions over large free energy barriers compared to fixed binning.
  • MAB improved the diversity of sampled pathways and the accuracy of rate constant estimation.

Conclusions:

  • The minimal adaptive binning scheme offers a simple yet effective solution for rare-event simulations.
  • MAB enhances the efficiency and accuracy of weighted ensemble path sampling.
  • The scheme is broadly applicable to rare-event sampling strategies utilizing progress coordinates.