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Record W3184345541 · doi:10.1103/physrevb.102.235426

Berry curvature, orbital magnetization, and Nernst effect in biased bilayer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi mathvariant="normal">W</mml:mi><mml:msub><mml:mi>Se</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math>

2020· article· lv· W3184345541 on OpenAlexaff
Vassilios Vargiamidis, P. Vasilopoulos, M. Tahir, Neophytos Neophytou

Bibliographic record

VenuePhysical review. B./Physical review. B · 2020
Typearticle
Languagelv
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsConcordia University
FundersHorizon 2020
KeywordsBerry connection and curvatureNernst effectNernst equationCondensed matter physicsMagnetizationPhysicsCurvatureElectric fieldOrbital magnetizationBilayerMagnetic fieldQuantum mechanicsGeometric phaseGeometryChemistryMathematicsMagnetic anisotropyElectrode

Abstract

fetched live from OpenAlex

A valley-contrasting Berry curvature in bilayer transition metal dichalcogenides with spin-orbit coupling can generate valley magnetization when the inversion symmetry is broken, for example, by an electric field, regardless of time-reversal symmetry. A nontrivial Berry curvature can also lead to anomalous transport responses, such as the anomalous Hall effect and the anomalous Nernst effect. Applied to a bilayer $\mathrm{W}{\mathrm{Se}}_{2}$, an electric field can tune the Berry curvature and orbital magnetic moment, which has important consequences for the orbital magnetization and the anomalous Nernst responses. The orbital magnetization and its two contributions, one due to the magnetic moment and one due to the Berry curvature, are calculated and interpreted in terms of opposite circulating currents of the bands in the two layers. The valley anomalous Nernst coefficient and spin Nernst coefficient are also calculated. We find that a finite electric field leads to peaks and dips in the Nernst responses that have the signs of the Berry curvatures of the bands and are proportional to their magnitudes; it also enhances the valley Nernst responses. These experimentally verifiable findings may be promising for caloritronic applications.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0870.005

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 head0.020
GPT teacher head0.283
Teacher spread0.263 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations21
Published2020
Admission routes1
Has abstractyes

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