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Record W2021877173 · doi:10.1039/c2cp22949h

Molecular dynamics calculation of molecular volumes and volumes of activation

2012· article· en· W2021877173 on OpenAlexafffund
Jacob Spooner, Heather Wiebe, N. Boon, E. Deglint, Essex Edwards, Brandon Yanciw, Bruce L. Patton, L. Thiele, P. Dance, N. Weinberg

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2012
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsUniversity of the Fraser ValleySimon Fraser University
FundersWestern Canada Research Grid
KeywordsMolecular dynamicsChemistryTransition state theorySolventThermodynamicsTransition stateMoleculeComputational chemistryPhysical chemistryChemical physicsKineticsReaction rate constantOrganic chemistryCatalysisPhysics

Abstract

fetched live from OpenAlex

Experimentally, the effects of pressure on reaction rates are described by their pressure derivatives, known as volumes of activation. Transition state theory directly links activation volumes to partial molar volumes of reactants and transition states. We discuss a molecular dynamics method for the accurate calculation of molecular volumes, within which the volumes of molecular species are obtained as a difference between the volumes of pure solvent and solvent with a single molecule inserted. The volumes thus obtained depend on the molecular geometry, the strength and type of the solute-solvent interactions, as well as temperature and pressure. The partial molar volumes calculated using this approach agree well with experimental data. Since this method can also be applied to transition state species, it allows for quantitative analysis of experimental volumes of activation in terms of structural parameters of the corresponding transition states. The efficiency of the approach is illustrated by calculation of volumes of activation for three nonpolar reactions in nonpolar solvents. The results agree well with the experimental data.

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 categoriesMeta-epidemiology (narrow)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.229
Teacher spread0.222 · 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.

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

Citations31
Published2012
Admission routes2
Has abstractyes

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