A network model for field and quenched disorder effects in artificial spin ice

TL;DR

This paper models how external fields and quenched disorder influence the dynamics and configurational accessibility of artificial spin ice, revealing conditions for optimal control and potential data storage applications.

Contribution

It introduces a network model representing the configurational phase space of artificial spin ice, incorporating effects of field strength and disorder on state connectivity and reversibility.

Findings

Optimal field strengths maximize accessible states.

Disorder increases the fraction of accessible configurations.

Network connectivity correlates with controllability of spin ice states.

Abstract

We have performed a systematic study of the effects of field strength and quenched disorder on the driven dynamics of square artificial spin ice. We construct a network representation of the configurational phase space, where nodes represent the microscopic configurations and a directed link between node i and node j means that the field may induce a transition between the corresponding configurations. In this way, we are able to quantitatively describe how the field and the disorder affect the connectedness of states and the reversibility of dynamics. In particular, we have shown that for optimal field strengths, a substantial fraction of all states can be accessed using external driving fields, and this fraction is increased by disorder. We discuss how this relates to control and potential information storage applications for artificial spin ices.