MagSAF: Software for simulating and fitting magnetic hysteresis loops of synthetic antiferromagnets
Bibliographic record
Abstract
We have developed the macrospin-based software MagSAF that allows for fast and easy simulations of magnetic hysteresis loops of synthetic antiferromagnets with in-plane magnetization and in-plane uniaxial magnetic anisotropies. Additionally, the bilinear and biquadratic interlayer exchange coupling strengths can be extracted from experimental data via curve fitting. It also nurtures understanding the physics behind the magnetic hysteresis loops by allowing the user to examine the macrospin angle rotation and the energy landscape during the magnetic field sweep. We prepared CoFeB(5nm)/Ru(dRu)/CoFeB(5nm) synthetic antiferromagnets with 0.4nm≤dRu≤0.85nm to test our software. We find great agreement between our extracted bilinear and biquadratic coupling strengths and those that were obtained with a more advanced discrete energy model that takes the twisting of spins over the ferromagnetic layer thickness into account. For a thicker synthetic antiferromagnet CoFeB(18.0 nm)/Ru(0.55 nm)/CoFeB(5.75 nm), the differences between the macrospin and discrete energy model become more apparent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".