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>
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".