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Using tensor network states for multi-particle Brownian ratchets.

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Interacting particles in a flashing ratchet system exhibit altered transport properties. Increased particle density shifts the optimal driving frequency for maximum current, revealing the impact of interactions on nonequilibrium transport.

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Area of Science:

  • Statistical Mechanics
  • Nonlinear Dynamics
  • Condensed Matter Physics

Background:

  • Brownian ratchets demonstrate how time-periodic driving creates nonequilibrium transport.
  • Single-particle transport is understood via potential, but many-interacting-carrier systems are complex.
  • Experimental studies have moved beyond single-body cases to explore interacting particle systems.

Purpose of the Study:

  • To analyze the impact of particle interactions on the current in a flashing ratchet system.
  • To investigate nonequilibrium transport in a one-dimensional lattice model of volume-excluding particles.

Main Methods:

  • Employed the time-dependent variational principle applied to binary tree tensor networks.
  • Propagated a distribution over many-body configurations using a controllable variational approximation.
  • Validated calculations against Gillespie trajectory sampling.

Main Results:

  • Identified a shift in the frequency of maximum current towards higher driving frequencies.
  • Demonstrated that this shift is dependent on lattice occupancy (particle density).
  • Explained the influence of interactions on the ratchet's current.

Conclusions:

  • Particle interactions significantly alter transport dynamics in flashing ratchets.
  • Lattice occupancy is a critical factor influencing the optimal driving frequency for current generation.
  • Tensor network methods provide an effective approach to study complex many-body systems.