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Record W2969662642 · doi:10.1103/physreve.100.022128

Phase-field crystal for an antiferromagnet with elastic interactions

2019· article· en· W2969662642 on OpenAlexafffund
Niloufar Faghihi, Simiso K. Mkhonta, K. R. Elder, Martin Grant

Bibliographic record

VenuePhysical review. E · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSolidification and crystal growth phenomena
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsCondensed matter physicsFerrimagnetismAntiferromagnetismScalingFrustrationPhysicsPhase (matter)Phase transitionVortexCoupling (piping)Field (mathematics)Magnetic fieldMaterials scienceMagnetizationQuantum mechanicsGeometry

Abstract

fetched live from OpenAlex

We introduce a model which contains the essential elements to formulate and study antiferromagnetism, using the phase-field crystal framework. We focus on the question of how magneto-elastic coupling could lift the frustration in the two-dimensional hexagonal antiferromagnetic phase. Using simulations we observe a rich variety of different phases stable in this model. To characterize different phases we calculate the chiral order parameter and identify the scaling behavior of this order parameter. Furthermore, we observe that vortices appear and are stable close to the nonmagnetic defects. Finally, we studied the ferrimagnetic and spin-flop phase transition in the presence of an external magnetic field.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.031
GPT teacher head0.370
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations5
Published2019
Admission routes2
Has abstractyes

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