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Record W1971550944 · doi:10.1021/ma4005023

Conjugated Polymers: Evaluating DFT Methods for More Accurate Orbital Energy Modeling

2013· article· en· W1971550944 on OpenAlexaff
Theresa M. McCormick, Colin R. Bridges, Elisa I. Carrera, Paul M. DiCarmine, Gregory L. Gibson, Jon Hollinger, Lisa M. Kozycz, Dwight S. Seferos

Bibliographic record

VenueMacromolecules · 2013
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHOMO/LUMOConjugated systemDensity functional theoryPolymerGaussianMolecular orbitalAcceptorChemistryComputational chemistryMolecular physicsMaterials sciencePhysical chemistryThermodynamicsPhysicsMoleculeQuantum mechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

Density functional theory (DFT) calculations are useful to model orbital energies of conjugated polymers, yet discrepancy between theory and experiment exist. Here we evaluate a series of relatively straightforward calculation methods using the standard Gaussian 09 software package. Five calculations were performed on 22 different conjugated polymer model compounds at the B3LYP and CAM-B3LYP levels of theory and results compared with experiment. Chain length saturation occurs at approximately 6 and 4 repeat units for homo- and donor–acceptor type conjugated polymers, respectively. The frontier orbital energies are better approximated using B3LYP than CAM-B3LYP, and the HOMO energy can be reasonably correlated with experiment [mean signed error (MSE) = 0.22 eV]. The LUMO energies, however are poorly correlated (MSE = 0.59 eV), and we show that the molecular orbital energy of the triplet state gives a much better estimate of the experimentally determined LUMO level (MSE = −0.13 eV).

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)0.002
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.309
Teacher spread0.286 · 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

Citations216
Published2013
Admission routes1
Has abstractyes

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