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Record W2606802774 · doi:10.1021/acs.jpcc.7b03618

Benzene and Pyridine on Silicon (001): A Trial Ground for Long-Range Corrections in Density Functional Theory

2017· article· en· W2606802774 on OpenAlexafffund
Oliver Warschkow, Jennifer M. Bennett, Jill A. Miwa, Gregory P. Lopinski, Federico Rosei, David R. McKenzie, Nigel A. Marks

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsInstitut National de la Recherche ScientifiqueNational Research Council Canada
FundersCentre of Excellence for Quantum Computation and Communication Technology, Australian Research CouncilAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Computational InfrastructureAarhus Universitets ForskningsfondNatur og Univers, Det Frie Forskningsråd
KeywordsDensity functional theoryBenzeneRange (aeronautics)PyridineSiliconChemistryComputational chemistryMaterials scienceOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The adsorption chemistry of benzene and pyridine on the silicon (001) surface is characterized by two prominent adsorbate configurations: a precursor structure bonded to a single Si–Si dimer and a “tight-bridge” configuration that connects two adjacent dimers. We examine here the performance of 20 density functionals in predicting the relative stability of these two configurations. Discrepancies between the predicted and experimentally observed preferred structures highlight the importance of long-range exact-exchange terms in these adsorbate systems. These terms, however, tend to be detrimental to the prediction of adsorption and activation energies. We discuss this conundrum in terms of systematic exchange-correlation errors that scale with the number of molecule–surface bonds.

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 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.281
Teacher spread0.260 · 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

Citations3
Published2017
Admission routes2
Has abstractyes

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