Konstantin P. Skokov

First-order magnetostructural transitions underpin the functionality of many magnetocaloric materials and form the basis of emerging solid-state cooling technologies. However, their time-dependent response remains underexplored, despite being essential for understanding and further optimizing the magnetostructural transformation. Here, we developed a unique experimental setup to perform simultaneous measurements of magnetization, strain, and temperature change in the benchmark Heusler-alloy Ni-Mn-In under magnetic fields up to 30 T at sweep rates of 10 T ⋅ min ⁻¹ . By implementing a time-dependent measurement protocol, we access both the field-driven and time-dependent evolutions of magnetic and structural order parameters along the forward and reverse transition directions. While magnetization rapidly stabilizes after field halting, the probed strain response continues to evolve over extended timescales, indicating distinct relaxation behavior of the measured properties. Quantitative analysis using an extended Avrami-Hay model reveals a secondary diffusive contribution that is required to describe this slow strain evolution. These results provide a general framework for probing coupled order parameters in first-order multifunctional materials, offering insights for the development of efficient caloric devices.

The first-order magnetic transition in magnetocaloric materials involves structural, magnetic and electronic changes on atomic length scales leading to large effects in their bulk physical properties. However, the intermediate microstructural length scale remains widely unexplored and poorly described. In this study, the field-induced first-order magnetoelastic isostructural transition in (Mn,Fe)2(Si,P) is investigated by multiscale XMCD measurements. We take advantage of the magnetic contrast provided by X-ray magnetic circular dichroism (XMCD) and the possibility to focus the beam to a micrometric size to study how the field-induced transition develops locally. These local measurements are compared to the macroscopic magnetization and magnetostriction by simultaneously recording the signals from a Hall probe and from strain gauges, respectively. The micro-beam XMCD signals exhibit significant deviations from the macro-beam response, indicating pronounced spatial variations in the local field-induced transitions. In contrast, the macroscopic XMCD signal is consistent with the macroscopic magnetization measurements obtained from SQUID and Hall probe magnetometry, as well as with strain gauge data, yielding an average transition field of µ0Ht ≈ 2 T.

Emergent dynamics, which arise from local interactions between many elementary components, are central to complex physical and biological systems. However, realizing such dynamics in artificial materials, particularly under low-energy stimuli, remains a challenge. While dipole–dipole interactions are typically suppressed in magnetic storage, here we amplify and use them as a core mechanism to construct a strongly dipolar-coupled network of SmCo₅ macrospins at the wafer scale, which can exhibit intrinsic interaction-driven collective dynamics in response to voltage pulses. This network integrates three key ingredients: the strong dipole–dipole interaction, giant voltage control of coercivity over nearly 1000-fold, and a network topology with a frustrated Ising-like energy landscape. As a result, the network, when stimulated by ∼1 V pulses, transitions from a high-coercivity memory regime into a low-coercivity regime in which internal dipolar fields alone trigger collective magnetic reconfiguration. In this regime, the network exhibits emergent behaviors absent at the level of isolated macrospins, including spontaneous demagnetization, greatly enhanced magnetization modulation, reversible “freeze and resume” evolution, and stochastic convergence toward low-energy magnetic configurations. Furthermore, as a conceptual illustration, micromagnetic simulation of such a strongly dipolar-coupled network shows that the resulting high-dimensional collective dynamics can support temporal information processing, such as accurate chaotic Mackey–Glass prediction and multiclass drone-signal classification. Our work suggests a conceptually distinct route toward scalable, energy-efficient neuromorphic computing, one rooted in local physical interaction-driven emergent dynamics at the network level rather than merely mimicking individual neurons and synapses.

First-order magnetostructural transitions underpin the functionality of many magnetocaloric materials and form the basis of emerging solid-state cooling technologies. However, their time-driven response remains underexplored, despite containing intrinsic kinetic information essential for understanding and further optimizing the transformation dynamics. Here, we developed a unique experimental setup to perform simultaneous measurements of magnetization, strain, and temperature change in the benchmark Heusler-alloy Ni–Mn–In under magnetic fields up to 30 T at sweep rates of 10 T/min. By implementing a kinetic measurement protocol, we access both the field-driven and time-driven evolutions of magnetic and structural order parameters along the forward and reverse transition directions. While magnetization rapidly stabilizes after field halting, the probed strain response continues to evolve over extended timescales, indicating distinct relaxation behavior of the measured properties. Quantitative analysis using an extended Avrami–Hay model reveals a secondary diffusive contribution that is required to describe this slow strain evolution. This long-term kinetic dominance of strain also coincides with the substantial structural entropy change characteristic of the Ni–Mn–X family, relating the primary entropy contributor to the strain’s extended response. These results provide a general framework for probing coupled order parameters in first-order multifunctional materials, offering insights for the development of efficient caloric devices.

Permanent magnets draw their properties from a complex interplay of chemical composition and phase, each with their associated intrinsic magnetic properties. Gaining an understanding of these interactions is the key to deciphering the origins of a permanent magnets’ magnetic performance and facilitate the engineering of much improved-performing magnets. Here, we use advanced multiscale microscopy and microanalysis on a bulk Sm ₂ (Co,Fe,Cu,Zr) ₁₇ pinning-type high-performance magnet with outstanding thermal and chemical stability. Comparison of the microstructure in regions of different composition, we demonstrate that the pinning of magnetic domains, imaged by nanoscale magnetic induction mapping, is controlled by the composition and atomic arrangement of copper. This is confirmed by micromagnetic simulations. Contrary to the belief that grain boundaries are “weak links” in magnetic materials, we demonstrate grain boundaries undergo magnetization reversal at relatively low fields (0.1-0.3 T), but this remains confined to these regions and does not significantly impact the magnet’s coercivity. Our results showcase that it is the optimal microstructure within the grain itself that is crucial for achieving the desired magnetic properties.