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Record W2012852497 · doi:10.1021/jp0129737

DFT Calculations of Core−Electron Binding Energies of the Peptide Bond

2001· article· en· W2012852497 on OpenAlexafffund
Delano P. Chong, Philippe Aplincourt, Christophe Bureau

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAbsolute deviationFormamideChemistryBinding energyStandard deviationMoleculeCore electronElectronComputational chemistryAtomic physicsPhysicsMathematicsStatisticsQuantum mechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

Although an efficient DFT method using the generalized transition-state model to calculate core−electron binding energies had been successfully applied to over 200 cases, with an average absolute deviation of only 0.21 eV from experiment, a new Δ E KS (PW86−PW91)/cc-pCVTZ model based on total Kohn−Sham energy difference was recently developed. Not only was the model error-free, but also the average absolute deviation for 32 cases studied was found to be 0.15 eV. In this study, we first confirm the excellent performance of such a Δ E KS approach with 46 new cases, with the result that the average absolute deviation from experiment for the 78 cases remains at 0.15 eV. With such consistent accuracy, this new method is applied to the peptide bond. The model molecules studied in this work include formamide, N -methylformamide, N, N -dimethylformamide, acetamide, N -methylacetamide, N, N -dimethylacetamide, and two model dipeptides, one cyclic and one acyclic. The difference in the computed nitrogen core−electron binding energy between the two model dipeptides is found to be 0.85 eV, several times our average absolute deviation. This may be of interest to other workers studying other aspects of the peptide bond.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.288

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 science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations40
Published2001
Admission routes2
Has abstractyes

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