Entropy of self-avoiding branching polymers: mean field theory and Monte Carlo simulations

TL;DR

This paper develops a mean field theory and uses Monte Carlo simulations to analyze the entropy and branching properties of self-avoiding polymers modeled as trees on lattices, revealing the theory's high accuracy especially in higher dimensions.

Contribution

The paper introduces a mean field approach to compute entropy and branch-node statistics of self-avoiding trees, validated by Monte Carlo simulations across multiple dimensions.

Findings

Mean number of branch-nodes depends only on chemical potential.

Mean field theory accurately predicts entropy in various dimensions.

Higher dimensions show better agreement between theory and simulations.

Abstract

We study the statistics of branching polymers with excluded-volume interactions, by modeling them as single self-avoiding trees on a generic regular periodic lattice with coordination number $q$. Each lattice site can be occupied at most by one tree node, and the fraction of occupied sites can vary from dilute to dense conditions. By adopting the statistics of rooted-directed trees as a proxy for that of undirected trees without internal loops and by an exact mapping of the model into a field theory, we compute the entropy and the mean number of branch-nodes within a mean field approximation and in the thermodynamic limit. In particular, we find that the mean number of branch-nodes is independent of both the lattice details and the lattice occupation, depending only on the associated chemical potential. Monte Carlo simulations in $d=2,3,4$ provide evidence of the remarkable accuracy of the mean field theory, more accurate for higher dimensions.