Modeling magnetization reversal in multilayers with interlayer exchange coupling
Bibliographic record
Abstract
Spin spirals form inside the magnetic layers of antiferromagnetic and noncollinearly coupled magnetic multilayers in the presence of an external field. This spin structure can be modeled to extract the direct exchange stiffness of the magnetic layers and the strength of the interlayer exchange coupling across the spacer layer. In this article, we discuss three models which describe the evolution of the spin spiral with the strength of the external magnetic field in these coupled structures: discrete energy, discrete torque, and continuous torque. These models are expanded to accommodate multilayers with any number of ferromagnetic layers, any combination of material parameters, and asymmetry. We compare their performance when fitting to the measured magnetization data of a range of sputtered samples with one or multiple ferromagnetic layers on either side of the spacer. We find that the discrete models produce better fits than the continuous for asymmetric and multiferromagnetic structures and exhibit much better computational scaling with high numbers of atomic layers than the continuous model. For symmetric, single-layered structures, the continuous model produces the same fit statistics and outperforms the discrete models. Last, we demonstrate methods to use interfacial layers to measure the exchange stiffness of magnetic layers with low interlayer exchange coupling. An open-access website has been provided to allow the fitting of magnetization as a function of field in arbitrary coupled structures using the discrete energy model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".