Topology-Induced Inverse Phase Transitions

TL;DR

This paper investigates inverse phase transitions, where systems become more disordered when cooled, analyzing their emergence in heterogeneous networks with disassortative correlations through analytical and numerical methods.

Contribution

It provides a combined analytical and numerical study of inverse phase transitions, highlighting the role of network heterogeneity and degree correlations in these phenomena.

Findings

Inverse transitions occur in tricritical systems on heterogeneous networks.

Disassortative degree correlations enhance inverse transition effects.

Microscopic interpretation involves freezing of sparse subgraphs and coupling renormalization.

Abstract

Inverse phase transitions are striking phenomena in which an apparently more ordered state disorders under cooling. This behavior can naturally emerge in tricritical systems on heterogeneous networks and it is strongly enhanced by the presence of disassortative degree correlations. We show it both analytically and numerically, providing also a microscopic interpretation of inverse transitions in terms of freezing of sparse subgraphs and coupling renormalization.