Transition path sampling in Ising models on heterogeneous graphs

TL;DR

This paper applies transition path sampling to study activation barriers in the Ising model on various heterogeneous graphs, revealing how disorder affects transition dynamics and introducing methods for better analysis.

Contribution

It introduces a minimal three-state kinetic model for interpreting transition probabilities and demonstrates its validation and application across different graph types.

Findings

Transition probabilities are influenced by intermediate configurations.

Sample-to-sample fluctuations are weak in random regular graphs.

Instance-dependent temperature rescaling improves finite-size scaling analysis.

Abstract

Activated transitions have rates that are often exponentially small in system size. Extracting the associated activation barriers is challenging in practice, especially in the deeply metastable regimes and in the presence of disorder. Here, we use transition path sampling to evaluate transition probabilities between ferromagnetic states in the Ising model on finite sparse random graphs, which are perhaps the simplest example of a disordered system with metastable states. To interpret the transient onset of the transition probability curve, we introduce a minimal three-state kinetic description that highlights the role of intermediate configurations. We validate the method on the heterogeneous Zachary Karate Club network, where distinct dynamical regimes emerge as temperature varies. We then apply the method to random regular graphs and Erd\H{o}s-R\'{e}nyi graphs, showing that sample-to-sample fluctuations are weak in the former but that quenched topological disorder induces sizable instance variability in the latter. For Erd\H{o}s-R\'{e}nyi graphs, we introduce an instance-dependent temperature rescaling that restores a consistent finite-size scaling of dynamical rates and enables a direct comparison with the corresponding static free-energy barrier.