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Record W2009412440 · doi:10.1103/physrevb.84.201402

Electronic energy level alignment at metal-molecule interfaces with a<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>G</mml:mi><mml:mi>W</mml:mi></mml:mrow></mml:math>approach

2011· article· lv· W2009412440 on OpenAlexafffund
Isaac Tamblyn, Pierre Darancet, Su Ying Quek, Stanimir Bonev, Jeffrey B. Neaton

Bibliographic record

VenuePhysical Review B · 2011
Typearticle
Languagelv
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaOffice of ScienceBasic Energy SciencesNational Energy Research Scientific Computing CenterU.S. Department of Energy
KeywordsIonization energyDensity functional theoryWork functionEnergy (signal processing)Electronic structureAtomic physicsRandom phase approximationPhysicsIonizationCondensed matter physicsQuantum mechanicsElectrode

Abstract

fetched live from OpenAlex

Using density functional theory and many-body perturbation theory within a $GW$ approximation, we calculate the electronic structure of a metal-molecule interface consisting of benzene diamine (BDA) adsorbed on Au(111). Through direct comparison with photoemission data, we show that a conventional ${G}_{0}{W}_{0}$ approach can underestimate the energy of the adsorbed molecular resonance relative to the Au Fermi level by up to 0.8 eV. The source of this discrepancy is twofold: a 0.7 eV underestimate of the gas phase ionization energy (IE), and a 0.2 eV overestimate of the Au work function. Refinements to self-energy calculations within the $GW$ framework that account for deviations in both the Au work function and BDA gas-phase IE can result in an interfacial electronic level alignment in quantitative agreement with experiment.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.232
Teacher spread0.213 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations83
Published2011
Admission routes2
Has abstractyes

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