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Record W4385774588 · doi:10.48550/arxiv.2308.05169

Interacting holes in gated WSe$_2$ quantum dots

2023· preprint· en· W4385774588 on OpenAlexfundno aff
Daniel Miravet, Abdulmenaf Altıntaş, Alina Wania Rodrigues, Maciej Bieniek, Marek Korkusiński, Paweł Hawrylak

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNarodowa Agencja Wymiany AkademickiejAlliance de recherche numérique du CanadaUniversity of Ottawa
KeywordsQuantum dotPhysicsCondensed matter physicsAtomic orbitalValence (chemistry)Electronic structureSpin (aerodynamics)Wave functionGround stateMolecular physicsQuantum mechanicsElectron

Abstract

fetched live from OpenAlex

We develop here a theory of the electronic properties of a finite number of valence holes in gated WSe$_2$ quantum dots, considering the influence of spin, valley, electronic orbitals, and many-body interactions. The single-particle wave functions are constructed by combining the spin-up and down states of the highest valence bulk bands employing a multi-million atom ab-initio based tight-binding model solved in the wave-vector space, allowing to study up to 100 nm radius quantum dots atomistically. The effects of the many-body interactions are determined using the configuration interaction (CI) technique, applied up to $N = 6$ holes occupying up to 6 electronic shells with 42 orbitals. Our results show that N=2 holes are in valley and spin anti-ferromagnetic ground state, independent of the interaction strength and the quantum dot size. However, we predict that higher number of holes can undergo a transition to spontaneously broken symmetry valley and spin polarized ferromagnetic phases, highlighting the interplay between the many-body effects and the quantum dot lateral size and confining potential depth.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.103
GPT teacher head0.225
Teacher spread0.121 · 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 teacher head, not a consensus.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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