A voltage-responsive strongly dipolar-coupled macrospin network with emergent dynamics for computing
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arXiv
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2025
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| author | Ye, Xinglong Zhao, Zhibo Wang, Qian Li, Jiangnan Maccari, Fernando Lu, Ning Dietz, Christian Adabifiroozjaei, Esmaeil Molina-Luna, Leopoldo Tian, Yufeng Bai, Lihui Wang, Guodong Skokov, Konstantin Chen, Yanxue Yan, Shishen Kruk, Robert Hahn, Horst Gutfleisch, Oliver |
| author_facet | Ye, Xinglong Zhao, Zhibo Wang, Qian Li, Jiangnan Maccari, Fernando Lu, Ning Dietz, Christian Adabifiroozjaei, Esmaeil Molina-Luna, Leopoldo Tian, Yufeng Bai, Lihui Wang, Guodong Skokov, Konstantin Chen, Yanxue Yan, Shishen Kruk, Robert Hahn, Horst Gutfleisch, Oliver |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20906 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A voltage-responsive strongly dipolar-coupled macrospin network with emergent dynamics for computing Ye, Xinglong Zhao, Zhibo Wang, Qian Li, Jiangnan Maccari, Fernando Lu, Ning Dietz, Christian Adabifiroozjaei, Esmaeil Molina-Luna, Leopoldo Tian, Yufeng Bai, Lihui Wang, Guodong Skokov, Konstantin Chen, Yanxue Yan, Shishen Kruk, Robert Hahn, Horst Gutfleisch, Oliver Materials Science Mesoscale and Nanoscale Physics 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. |
| title | A voltage-responsive strongly dipolar-coupled macrospin network with emergent dynamics for computing |
| topic | Materials Science Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2512.20906 |