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Record W2996952011 · doi:10.1103/physrevb.101.125423

Band nesting and exciton spectrum in monolayer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>MoS</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math>

2020· article· lv· W2996952011 on OpenAlexafffund
Maciej Bieniek, Ludmiła Szulakowska, Paweł Hawrylak

Bibliographic record

VenuePhysical review. B./Physical review. B · 2020
Typearticle
Languagelv
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaNarodowe Centrum Nauki
KeywordsExcitonExcited statePhysicsTopology (electrical circuits)Atomic physicsCondensed matter physicsMathematicsCombinatorics

Abstract

fetched live from OpenAlex

We discuss here the effect of band nesting and topology on the spectrum of excitons in a single layer of ${\mathrm{MoS}}_{2}$, a prototype transition metal dichalcogenide material. We solve for the single-particle states using the ab initio based tight-binding model containing metal $d$ and sulfur $p$ orbitals. The metal orbitals' contribution evolving from $K$ to $\mathrm{\ensuremath{\Gamma}}$ points results in conduction-valence band nesting and a set of second minima at $Q$ points in the conduction band. There are three $Q$ minima for each $K$ valley. We accurately solve the Bethe-Salpeter equation including both $K$ and $Q$ points and obtain ground and excited exciton states. We determine the effects of the electron-hole single-particle energies including band nesting, direct and exchange screened Coulomb electron-hole interactions, and resulting topological magnetic moments on the exciton spectrum. The ability to control different contributions combined with accurate calculations of the ground and excited exciton states allows for the determination of the importance of different contributions and a comparison with effective mass and $k\ifmmode\cdot\else\textperiodcentered\fi{}p$ massive Dirac fermion models.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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 score0.862
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.005

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 head0.026
GPT teacher head0.295
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; 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

Citations26
Published2020
Admission routes2
Has abstractyes

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