Using tensor network states for multi-particle Brownian ratchets
Nils E Strand1, Hadrien Vroylandt1, Todd R Gingrich1
1Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, USA.
Abstract:
The study of Brownian ratchets has taught how time-periodic driving supports a time-periodic steady state that generates nonequilibrium transport. When a single particle is transported in one dimension, it is possible to rationalize the current in terms of the potential, but experimental efforts have ventured beyond that single-body case to systems with many interacting carriers. Working with a lattice model of volume-excluding particles in one dimension, we analyze the impact of interactions on a flashing ratchet's current. To surmount the many-body problem, we employ the time-dependent variational principle applied to binary tree tensor networks. Rather than propagating individual trajectories, the tensor network approach propagates a distribution over many-body configurations via a controllable variational approximation. The calculations, which reproduce Gillespie trajectory sampling, identify and explain a shift in the frequency of maximum current to higher driving frequency as the lattice occupancy increases.
More Related Videos
Related Concept Videos
Equilibrium Conditions for a Particle
To understand the concept of equilibrium, let us first consider the forces acting on an object. When different forces act on an object, they can...
Sequence Networks of Rotating Machines
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
First Law: Particles in One-dimensional Equilibrium
State Space Representation
Consider an RLC circuit, a...
First Law: Particles in Two-dimensional Equilibrium
Newton's first law tells us about...
Atomic Nuclei: Nuclear Spin State Population Distribution


