Memory of topologically constrained disorder in Shakti artificial spin ice

TL;DR

This paper explores how topologically constrained disorder in Shakti artificial spin ice leads to sequence-dependent memory effects, contrasting with the behavior of square spin ice, and highlights the role of topological constraints in generating robust memory in frustrated materials.

Contribution

It provides the first analytical and numerical investigation of memory effects in topologically constrained artificial spin ice, revealing path-dependent responses and the influence of interaction strength.

Findings

Shakti spin ice exhibits pronounced sequence-dependent responses.

Minimal supercells in Shakti show deterministic and stochastic memory effects.

Cyclic driving enhances memory retention in Shakti spin ice.

Abstract

Complex behaviors often sit at a critical threshold between order and disorder. But not all disorder is created equal. Disorder can be trivial or constrained, and correlated disorder can even be topological. Crucially, constrained disorder can harbor memory, leading to non-trivial, sequence-dependent responses to external manipulations. And yet the fascinating subject of "memory of disorder" remains poorly explored, as memory is often associated to the retention of metastable order. In recent years artificial frustrated materials -- in particular arrays of frustrated nanomagnets known as artificial spin ices -- have been employed to study complex disorders and its wealth of exotic behaviors, yet their memory properties have received much less attention. Here, we investigate both analytically and numerically the sequence-dependent responses of two somehow opposite yet related artificial spin ices: the Landau-ordered square spin ice and the disordered but topologically-ordered Shakti spin ice. We find that Shakti exhibits a pronounced sequence-dependent response, whereas in the square lattice, such path dependence is absent. Within Shakti, even the minimal periodic supercell demonstrates both deterministic and stochastic forms of sequence memory, depending on the interaction strength. Extending our study to cyclic driving, we find that retracing the same input path leads to enhanced memory retention. These results open new perspectives on how topological constraints and correlated disorder generate robust memory effects in frustrated artificial materials, hitherto examined mainly in terms of their ground-state kinetics and thermodynamics.