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Record W4300970013 · doi:10.1063/5.0118078

Magnetic field driven dynamics in twisted bilayer artificial spin ice at superlattice angles

2022· article· en· W4300970013 on OpenAlexafffund
Rehana Begum Popy, J. Howard Frank, R. L. Stamps

Bibliographic record

VenueJournal of Applied Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Condensed Matter Physics
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsSpin iceCondensed matter physicsNanomagnetBilayerMagnetic fieldDipoleSpin (aerodynamics)PhysicsSuperlatticeMagnetizationMaterials scienceChemistryMagnetic monopoleQuantum mechanics

Abstract

fetched live from OpenAlex

Geometrical designs of interacting nanomagnets have been studied extensively in the form of two-dimensional arrays called artificial spin ice. These systems are usually designed to create geometrical frustration and are of interest for the unusual and often surprising phenomena that can emerge. Advanced lithographic and element growth techniques have enabled the realization of complex designs that can involve elements arranged in three dimensions. Using numerical simulations employing the dumbbell approximation, we examine possible magnetic behaviors for bilayer artificial spin ice, in which the individual layers are rotated with respect to one another. The goal is to understand how magnetization dynamics are affected by long-range dipolar coupling that can be modified by varying the layer separation and layer alignment through rotation. We consider bilayers where the layers are both either square or pinwheel arrangements of islands. Magnetic reversal processes are studied and discussed in terms of domain and domain wall configurations of the magnetic islands. Unusual magnetic ordering is predicted for special angles that define lateral spin superlattices for the bilayer systems.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.960

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.238
Teacher spread0.228 · 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 designTheoretical or conceptual
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

Citations7
Published2022
Admission routes2
Has abstractyes

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