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Record W4416590185 · doi:10.1063/5.0288886

MagSAF: Software for simulating and fitting magnetic hysteresis loops of synthetic antiferromagnets

2025· article· en· W4416590185 on OpenAlexaff
Stephan Glamsch, Matthias Küß, Afan Terko, Andreas Hörner, Erol Girt, M. Albrecht

Bibliographic record

VenueJournal of Applied Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsSimon Fraser University
FundersDeutsche Forschungsgemeinschaft
KeywordsSpinsFerromagnetismBilinear interpolationCoupling (piping)HysteresisMagnetizationMagnetic hysteresisRotation (mathematics)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.234
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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