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Record W4323310670 · doi:10.1103/physrevb.107.125111

<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>IrF</mml:mi></mml:mrow><mml:mn>4</mml:mn></mml:msub></mml:math>: From tetrahedral compass model to topological semimetal

2023· article· lv· W4323310670 on OpenAlexaff
Chao Shang, Owen Ganter, Niclas Heinsdorf, Stephen M. Winter

Bibliographic record

VenuePhysical review. B./Physical review. B · 2023
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsUniversity of British Columbia
FundersWake Forest University
KeywordsMagnetismPhysicsHomogeneous spaceTopology (electrical circuits)Condensed matter physicsMachine learningCombinatoricsGeometryComputer scienceMathematics

Abstract

fetched live from OpenAlex

The intersection of topology, symmetry, and magnetism yields a rich structure of possible phases. In this work, we study theoretically the consequences of magnetism on ${\mathrm{IrF}}_{4}$, which was recently identified as a possible candidate topological nodal chain semimetal in the absence of magnetic order. We show that the spin-orbital nature of the Ir moments gives rise to strongly anisotropic magnetic couplings resembling a tetrahedral compass model on a diamond lattice. The predicted magnetic ground state preserves key symmetries protecting the nodal lines, such that they persist into the ordered phase at the mean-field level. The consequences for other symmetry reductions are also discussed.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.278
Threshold uncertainty score0.931

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2780.120

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 head0.030
GPT teacher head0.299
Teacher spread0.269 · 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 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

Citations6
Published2023
Admission routes1
Has abstractyes

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