Entropy-driven order in an array of nanomagnets

Long-range ordering is typically associated with a decrease in entropy. Yet, it can also be driven by increasing entropy in certain special cases. Here we demonstrate that artificial spin-ice arrays of single-domain nanomagnets can be designed to produce such entropy-driven order. We focus on the tetris artificial spin-ice structure, a highly frustrated array geometry with a zero-point Pauling entropy, which is formed by selectively creating regular vacancies on the canonical square ice lattice. We probe thermally active tetris artificial spin ice both experimentally and through simulations, measuring the magnetic moments of the individual nanomagnets. We find two-dimensional magnetic ordering in one subset of these moments, which we demonstrate to be induced by disorder (that is, increased entropy) in another subset of the moments. In contrast with other entropy-driven systems, the discrete degrees of freedom in tetris artificial spin ice are binary and are both designable and directly observable at the microscale, and the entropy of the system is precisely calculable in simulations. This example, in which the system’s interactions and ground-state entropy are well defined, expands the experimental landscape for the study of entropy-driven ordering.

“Entropy-driven order in an array of nanomagnets”, Hilal Saglam, Ayhan Duzgun, Aikaterini Kargioti, Nikhil Harle, Xiaoyu Zhang, Nicholas S. Bingham, Yuyang Lao, Ian Gilbert, Joseph Sklenar, Justin D. Watts, Justin Ramberger, Daniel Bromley, Rajesh V. Chopdekar, Liam O’Brien, Chris Leighton, Cristiano Nisoli and Peter Schiffer, Nature Physics 18, (2022).