MétaCan
Menu
Back to cohort
Record W4282577288 · doi:10.3389/fchem.2022.906674

Molecular Interactions From the Density Functional Theory for Chemical Reactivity: The Interaction Energy Between Two-Reagents

2022· article· en· W4282577288 on OpenAlexafffund
Ramón Alain Miranda‐Quintana, Farnaz Heidar‐Zadeh, Stijn Fias, Allison E. A. Chapman, Shubin Liu, Christophe Morell, Tatiana Gómez, Carlos Cárdenas, Paul W. Ayers

Bibliographic record

VenueFrontiers in Chemistry · 2022
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsMcMaster UniversityQueen's University
FundersFondo Nacional de Desarrollo Científico y TecnológicoNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCompute CanadaUniversity of Florida
KeywordsReactivity (psychology)ReagentChemistryDensity functional theoryComputational chemistryCombinatorial chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Reactivity descriptors indicate where a reagent is most reactive and how it is most likely to react. However, a reaction will only occur when the reagent encounters a suitable reaction partner. Determining whether a pair of reagents is well-matched requires developing reactivity rules that depend on both reagents. This can be achieved using the expression for the minimum-interaction-energy obtained from the density functional reactivity theory. Different terms in this expression will be dominant in different circumstances; depending on which terms control the reactivity, different reactivity indicators will be preferred.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.002

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 head0.021
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

Citations21
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in ChemistrySame topicComputational Drug Discovery MethodsFrench-language works237,207