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Record W4404465511 · doi:10.1103/physrevb.110.195135

Magnetic phase diagram of a two-orbital model for bilayer nickelates with varying doping

2024· article· en· W4404465511 on OpenAlexaff
Ling-Fang Lin, Yang Zhang, Nitin Kaushal, Gonzalo Álvarez, Thomas Maier, Adriana Moreo, Elbio Dagotto

Bibliographic record

VenuePhysical review. B./Physical review. B · 2024
Typearticle
Languageen
FieldMaterials Science
TopicIron-based superconductors research
Canadian institutionsUniversity of British Columbia
FundersBasic Energy SciencesOffice of ScienceU.S. Department of Energy
KeywordsCondensed matter physicsPhase diagramAntiferromagnetismFerromagnetismCoupling (piping)BilayerPhysicsSuperconductivityPhase (matter)Inductive couplingDopingMaterials scienceChemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Motivated by the discovery of bilayer nickelate superconductor La${}_{3}$Ni${}_{2}$O${}_{7}$, the authors investigate here magnetic phase diagrams with varying electronic densities $n$ in a bilayer 2\ifmmode\times\else\texttimes\fi{}2\ifmmode\times\else\texttimes\fi{}2 cluster. Rich magnetic states are observed, which can be understood by three main coupling mechanisms: (1) half-empty ferromagnetic (FM) coupling, (2) half-half antiferromagnetic (AFM) coupling, and (3) half-full FM coupling. At the half-filling case, the half-half mechanism leads to a robust (\ensuremath{\pi}, \ensuremath{\pi}, \ensuremath{\pi}) state. For the La${}_{3}$Ni${}_{2}$O${}_{7}$ ($n$=1.5) case, the AFM coupling is found to be strong along the $z$ axis, while the $x\phantom{\rule{0}{0ex}}y$ plane shows strong competition between AFM and FM tendencies.

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 score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.046
GPT teacher head0.434
Teacher spread0.388 · 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

Citations16
Published2024
Admission routes1
Has abstractyes

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Same venuePhysical review. B./Physical review. BSame topicIron-based superconductors researchFrench-language works237,207