The structure of a material is an important factor in determining its physical properties. Here, we adjust the structure of the Ni50Mn37Ga13 spun ribbons by changing the wheel speed to regulate the exchange bias effect of the material. The characterization results of micromorphology and structure show that as the wheel speed increases, the martensite lath decreases from 200 nm to 50 nm, the structure changed from the NM to a NM and 10M mixed martensitic structure containing mainly NM, then changed to NM and 10M where 10M and NM are approaching. Meanwhile, HE first increased and then decreased as the wheel speed increased. The optimum exchange bias effect (HE = 7.2 kOe) occurs when the wheel speed is 25 m∙s-1, mainly attributed to the enhanced ferromagnetism caused by part of 10M in NM martensite, which enhanced the exchange coupling of ferromagnetism and antiferromagnetism. This work reveals the structural dependence of exchange bias and provides a way to tune the magnitude of the exchange bias of Heusler alloys.
Hamiltonian parameters estimation is crucial in condensed matter physics, but is time- and cost-consuming. High-resolution images provide detailed information of underlying physics, but extracting Hamiltonian parameters from them is difficult due to the huge Hilbert space. Here, a protocol for Hamiltonian parameters estimation from images based on a machine learning (ML) architecture is provided. It consists in learning a mapping between spin configurations and Hamiltonian parameters from a small amount of simulated images, applying the trained ML model to a single unexplored experimental image to estimate its key parameters, and predicting the corresponding materials properties by a physical model. The efficiency of the approach is demonstrated by reproducing the same spin configuration as the experimental one and predicting the coercive field, the saturation field, and even the volume of the experiment specimen accurately. The proposed approach paves a way to achieve a stable and efficient parameters estimation.
The exchange bias effect obtained after zero field cooling (ZEB) not only saves energy, but also makes the device easier to control and reduces the size of the device. In this work, ZEB effect under different measurement field is obtained in Ni 50 Mn 37 Ga 13 alloy, further, combining the macroscopic magnetic test and phase field simulation, the microscopic magnetic states under different measurement fields are studied. Through phase field simulation, it is revealed that the density of ferromagnetic (FM)/antiferromagnetic (AFM) interface first expands and then shrinks with the increase of measurement field, which thus explains the optimum ZEB at an intermediate measurement field. This work reveals the important role of FM/AFM interface in the ZEB effect.
Many properties of materials exhibit a heavy dependency on the domain/grain size due to the change in interface density. Here, we show that in bulk Ni2Mn1+xGa1−x alloys (0.4 < x < 0.7), the exchange bias (EB) effect appears in the reentrant spin glass region and the magnitude of exchange bias (HEB) depends on the size of the ferromagnetic (FM) cluster in the antiferromagnetic (AFM) matrix. It was found that HEB first increases and then deceases as the size of the FM cluster decreases, which shows a non-monotonical relationship with the FM cluster size, and the relationship is similar to the grain size dependence of material properties such as the mechanical strength of metals and dielectric permittivity of ferroelectric ceramics. Further phase field simulation results repeat this phenomenon and illustrate that the change in EB can be attributed to the change in density of the FM/AFM interface, which provides a regulatable extra bias field through the Dzyaloshinskii–Moriya interaction. This work provides a method to tune HEB in bulk materials and reveals the mechanism of the dependency of EB on the FM cluster size, which could guide the design of bulk exchange-bias materials.
Hamiltonian parameter estimation is crucial in condensed matter physics, but time and cost consuming in terms of resources used. With advances in observation techniques, high-resolution images with more detailed information are obtained, which can serve as an input to machine learning (ML) algorithms to extract Hamiltonian parameters. However, the number of labeled images is rather limited. Here, we provide a protocol for Hamiltonian parameter estimation based on a machine learning architecture, which is trained on a small amount of simulated images and applied to experimental spin configuration images. Sliding windows on the input images enlarges the number of training images; therefore we can train well a neural network on a small dataset of simulated images which are generated adaptively using the same external conditions such as temperature and magnetic field as the experiment. The neural network is applied to the experimental image and estimates magnetic parameters efficiently. We demonstrate the success of the estimation by reproducing the same configuration from simulation and predict a hysteresis loop accurately. Our approach paves a way to a stable and general parameter estimation.
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