Information Dynamics at a Phase Transition

TL;DR

This paper introduces a novel information-theoretic approach using configurational entropy to analyze phase transitions, revealing how information storage and exchange peak at criticality in a lattice model.

Contribution

It applies configurational entropy to characterize critical behavior in phase transitions, linking information theory with statistical physics in a new way.

Findings

CE reaches a minimum at criticality.

CE exhibits three scaling regimes: scale free, turbulent, and critical.

Information processing is maximized at the phase transition.

Abstract

We propose a new way of investigating phase transitions in the context of information theory. We use an information-entropic measure of spatial complexity known as configurational entropy (CE) to quantify both the storage and exchange of information in a lattice simulation of a Ginzburg-Landau model with a scalar order parameter coupled to a heat bath. The CE is built from the Fourier spectrum of fluctuations around the mean-field and reaches a minimum at criticality. In particular, we investigate the behavior of CE near and at criticality, exploring the relation between information and the emergence of ordered domains. We show that as the temperature is increased from below, the CE displays three essential scaling regimes at different spatial scales: scale free, turbulent, and critical. Together, they offer an information-entropic characterization of critical behavior where the storage and processing of information is maximized at criticality.