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Hard-disk dipoles and non-reversible Markov chains.

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Event-chain Monte Carlo (ECMC) algorithms significantly accelerate molecular simulations for dipolar systems. Newtonian ECMC shows particular promise for overcoming simulation challenges in complex models.

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Area of Science:

  • Computational physics
  • Molecular dynamics
  • Statistical mechanics

Background:

  • Molecular simulation requires efficient algorithms to model complex systems.
  • Event-chain Monte Carlo (ECMC) offers a potential speedup over traditional methods.
  • Tethered hard-disk dipoles in 2D serve as a model system for studying dynamics.

Purpose of the Study:

  • To benchmark various event-chain Monte Carlo (ECMC) algorithms.
  • To evaluate ECMC's applicability to water models in molecular simulation.
  • To characterize dipole rotation dynamics using autocorrelation times.

Main Methods:

  • Benchmarking of non-reversible ECMC algorithms: straight, reflective, forward, and Newtonian.
  • Comparison with the reversible Metropolis algorithm using single-disk moves.
  • Analysis of dipole rotation dynamics via integrated autocorrelation times of polarization.

Main Results:

  • All tested ECMC algorithms demonstrated considerable speedups compared to the Metropolis algorithm.
  • Significant performance differences were observed among the ECMC variants.
  • Newtonian ECMC proved effective in mitigating dynamical arrest issues seen in other ECMC methods.

Conclusions:

  • ECMC algorithms offer substantial acceleration for molecular simulations of dipolar systems.
  • Newtonian ECMC is a promising variant for overcoming simulation bottlenecks, especially for complex, 3D models.
  • These findings support the application of ECMC to advanced molecular models, including water.