A voltage-responsive strongly dipolar-coupled macrospin network with emergent dynamics for computing

Emergent behavior, which arises from local interactions between simple elements, is pervasive in nature. It underlies the energy-efficient computing in our brains. However, realizing such dynamics in artificial materials, particularly under low-energy stimuli, remains a fundamental challenge. While dipole-dipole interactions are typically suppressed in magnetic storage, here we harness and amplify them to construct a strongly dipolar-coupled network of SmCo5 macrospins at wafer scale, which can exhibit intrinsic interaction-driven collective dynamics in response to voltage pulses. The network combines three essential ingredients: strong dipolar coupling by large single-domain macrospin, giant voltage control of coercivity over nearly 1000-fold, and disordered network topology with frustrated Ising-like energy landscape. When stimulated by 1 V pulses, the network enters a regime where interaction-driven magnetic behaviors emerge, including spontaneous demagnetization, greatly enhanced magnetization modulation, reversible freeze and resume evolution and stochastic convergence toward low-energy magnetic configurations. All these behaviors are completely absent at the single-nanomagnet level. Furthermore, by constructing micromagnetic models of the strongly dipolar-coupled macrospin networks, we show that the resulting nonlinear, high-dimensional collective dynamics, intrinsic to strongly-interacting systems, can enable accurate chaotic Mackey-Glass prediction and multiclass drone-signal classification. Our work establishes the voltage-responsive strongly-coupled SmCo5 network as a mesoscopic platform for probing emergent magnetic dynamics previously inaccessible under ambient conditions. It also suggests a fundamental distinct route towards scalable, low-voltage computing, one rooted in native physical interaction-driven collective dynamics at the network level.