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Record W2048800743 · doi:10.1103/physrevb.84.241201

<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msup><mml:mi>G</mml:mi><mml:mn>0</mml:mn></mml:msup><mml:msup><mml:mi>W</mml:mi><mml:mn>0</mml:mn></mml:msup></mml:mrow></mml:math>band gap of ZnO: Effects of plasmon-pole models

2011· article· lv· W2048800743 on OpenAlexafffund
Martin Stankovski, Gabriel Antonius, David Waroquiers, Anna Miglio, Hemant Dixit, Kiroubanand Sankaran, Matteo Giantomassi, Xavier Gonze, Michel Côté, Gian‐Marco Rignanese

Bibliographic record

VenuePhysical Review B · 2011
Typearticle
Languagelv
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversité de Montréal
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaFonds Wetenschappelijk OnderzoekFonds De La Recherche Scientifique - FNRS
KeywordsPhysicsComputer scienceAlgorithm

Abstract

fetched live from OpenAlex

Carefully converged calculations are performed for the band gap of ZnO within many-body perturbation theory (${G}^{0}{W}^{0}$ approximation). The results obtained using four different well-established plasmon-pole models are compared with those of explicit calculations without such models (the contour-deformation approach). This comparison shows that, surprisingly, plasmon-pole models depending on the $f$-sum rule gives less precise results. In particular, it confirms that the band gap of ZnO is underestimated in the ${G}^{0}{W}^{0}$ approach as compared to experiment, contrary to the recent claim of Shih et al. [Phys. Rev. Lett. 105, 146401 (2010)].

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.619

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0040.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.5660.282

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 head0.031
GPT teacher head0.259
Teacher spread0.228 · 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.

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

Citations125
Published2011
Admission routes2
Has abstractyes

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Same venuePhysical Review BSame topicZnO doping and propertiesFrench-language works237,207