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Record W3214277338 · doi:10.1038/s41467-023-39883-7

Long-range, non-local switching of spin textures in a frustrated antiferromagnet

2023· article· en· W3214277338 on OpenAlexaff
Shannon C. Haley, Eran Maniv, Shan Wu, Tessa Cookmeyer, Susana Torres-Londono, Meera Aravinth, Nikola Maksimovic, Joel E. Moore, R. J. Birgeneau, James G. Analytis

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsCanadian Institute for Advanced Research
FundersOffice of ScienceBasic Energy SciencesGordon and Betty Moore FoundationU.S. Department of Energy
KeywordsSpintronicsCondensed matter physicsAntiferromagnetismSpin (aerodynamics)Spin engineeringTexture (cosmology)Angular momentumPhysicsCoupling (piping)Degenerate energy levelsInformation storageLeverage (statistics)Spin polarizationMaterials scienceFerromagnetismQuantum mechanicsComputer scienceElectron

Abstract

fetched live from OpenAlex

Abstract Antiferromagnetic spintronics is an emerging area of quantum technologies that leverage the coupling between spin and orbital degrees of freedom in exotic materials. Spin-orbit interactions allow spin or angular momentum to be injected via electrical stimuli to manipulate the spin texture of a material, enabling the storage of information and energy. In general, the physical process is intrinsically local: spin is carried by an electrical current, imparted into the magnetic system, and the spin texture will then rotate in the region of current flow. In this study, we show that spin information can be transported and stored “non-locally" in the material Fe x NbS 2 . We propose that collective modes can manipulate the spin texture away from the flowing current, an effect amplified by strong magnetoelastic coupling of the ordered state. This suggests a novel way to store and transport spin information in strongly spin-orbit coupled magnetic 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.301
Teacher spread0.284 · 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 designObservational
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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueNature CommunicationsSame topicMagnetic properties of thin filmsFrench-language works237,207