Understanding spin textures in magnetic systems is extremely important to the spintronics and it is vital to extrapolate the magnetic Hamiltonian parameters through the experimentally determined spin. It can provide a better complementary link between theories and experimental results. We demonstrate deep learning can quantify the magnetic Hamiltonian from magnetic domain images. To train the deep neural network, we generated domain configurations with Monte Carlo method. The errors from the estimations was analyzed with statistical methods and confirmed the network was successfully trained to relate the Hamiltonian parameters with magnetic structure characteristics. The network was applied to estimate experimentally observed domain images. The results are consistent with the reported results, which verifies the effectiveness of our methods. On the basis of our study, we anticipate that the deep learning techniques make a bridge to connect the experimental and theoretical approaches not only in magnetism but also throughout any scientific research.

Numerical generation of physical states is essential to all scientific research fields. The role of a numerical generator is not limited to understanding experimental results; it can also be employed to predict or investigate characteristics of uncharted systems. A variational autoencoder model is devised and applied to a magnetic system to generate energetically stable magnetic states with low local deformation. The spin structure stabilization is made possible by taking the explicit magnetic Hamiltonian into account to minimize energy in the training process. A significant advantage of the model is that the generator can create a long‐range ordered ground state of spin configuration by increasing the role of stabilization even if the ground states are not necessarily included in the training process. It is expected that the proposed Hamiltonian‐guided generative model can bring about great advances in numerical approaches used in various scientific research fields.

Chiral magnetic domains are topological spin textures in which the Dzyaloshinskii–Moriya interaction assigns a given chirality to the domain walls. Notably, despite rapid progress in chiral magnetic research, one fundamental issue that remains unclear is how the chirality of chiral magnetic domains change as a magnetic field deforms the spin texture. Using spin‐polarized low energy electron microscopy, the evolution of Fe/Ni chiral magnetic stripe domains are investigated in single‐crystalline Fe/Ni/Cu/Co/Cu(001) multilayers in which the interlayer magnetic coupling between the Co and Fe/Ni films serves as an in‐plane magnetic field. Contrary to theoretical works, it is found that the chirality of the Néel walls results in a parallel alignment of the magnetic stripes with the in‐plane magnetic field direction. The transformation of chiral Néel walls into achiral Bloch walls can be precisely controlled by tuning the Cu spacer layer thickness. In addition, the domain wall exhibits a spontaneous asymmetry within the in‐plane magnetic field, leading to an unbalanced chirality between the left‐handed and right‐handed Bloch walls. These new results foster a better understanding of the chiral domain properties within a magnetic field.

No abstract

BackgroundTraumatic brain injury (TBI) is a major public health problem with high mortality and disability. Vitamin E, one of the antioxidants for treatment of TBI, has not been sufficiently evaluated for predicting prognosis of TBI. This study aimed to evaluate the prognostic value of vitamin E on functional outcomes of TBI patients with intracranial injury.MethodsA multi-center prospective cohort study was conducted in five university hospitals between 2018 and 2020. Adult TBI patients who visited the emergency department (ED) with intracranial hemorrhage or diffuse axonal injury confirmed by radiological examination were eligible. Serum vitamin E levels (mg/dL) were categorized into 4 groups: low (0.0–5.4), low-normal (5.5–10.9), high-normal (11.0–16.9), and high (17.0–). Study outcomes were set as 1- and 6-month disability (Glasgow outcome scale (GOS) 1–4). Multilevel logistic regression analysis was conducted to calculate the adjusted odds ratios (AORs) of vitamin E for related outcomes.ResultsAmong 550 eligible TBI patients with intracranial injury, the median (IQR) of serum vitamin E was 10.0 (8.0–12.3) mg/dL; 204/550 (37.1%) had 1-month disability and 197/544 (36.1%) had 6-month disability of GOS 1–4. Compared with the high-normal group, the odds of 1-month disability and 6-month disability increased in the low and low-normal group (AORs (95% CIs): 3.66 (1.62–8.27) and 2.60 (1.15–5.85) for the low group and 1.63 (1.08–2.48) and 1.60 (1.04–2.43) for the low-normal group, respectively).ConclusionLow serum vitamin E level was associated with poor prognosis at 1 and 6 months after TBI with intracranial injury.