MétaCan
Menu
Back to cohort
Record W3152958798 · doi:10.1002/jcc.26536

Modeling ligand electrochemical parameters by <scp>repulsion‐corrected</scp> eigenvalues

2021· article· en· W3152958798 on OpenAlexafffund
Pirouz Kiani, Elaine S. Dodsworth, A. B. P. Lever, William J. Pietro

Bibliographic record

VenueJournal of Computational Chemistry · 2021
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsLigand (biochemistry)LeverElectrochemistryChemistryEigenvalues and eigenvectorsComputational chemistryDensity functional theoryCombinatorial chemistryChemical physicsPhysical chemistryPhysicsQuantum mechanicsReceptorElectrode

Abstract

fetched live from OpenAlex

, more commonly known as Lever parameters, have played a major research role in understanding redox processes involved in inorganic electrochemistry, enzymatic reactions, catalysis, solar cells, biochemistry, and materials science. Despite their broad usefulness, Lever parameters are not well understood at a first-principles level. Using density functional theory, we demonstrate in this contribution that a ligand's Lever parameter is fundamentally related to the ligand's ability to alter the eigenvalue of the electroactive spin-orbital in an octahedral transition metal complex. Our analysis furthers a first-principles understanding of the nature of Lever parameters.

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 score0.175
Threshold uncertainty score0.623

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Computational ChemistrySame topicCO2 Reduction Techniques and CatalystsFrench-language works237,207