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Record W2021979852 · doi:10.1063/1.1782813

Transition path sampling study of classical rate-promoting vibrations

2004· article· en· W2021979852 on OpenAlexaff
Dimitri Antoniou, Mohammad Ramin Abolfath, Steven D. Schwartz

Bibliographic record

VenueThe Journal of Chemical Physics · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsNational Research Council Canada
FundersNational Institute of General Medical SciencesNational Science Foundation
KeywordsTransition state theoryOvershoot (microwave communication)VibrationQuantum tunnellingSaddle pointPath (computing)Transition rate matrixStatistical physicsQuantumPath integral formulationPhysicsSampling (signal processing)Chemical physicsControl theory (sociology)Classical mechanicsMathematicsQuantum mechanicsReaction rate constantComputer scienceKineticsStatistics

Abstract

fetched live from OpenAlex

It is now widely accepted that there is a class of enzymatic proton transfer reactions, which proceed through quantum tunneling. In a series of papers we have argued that some experimental features of these reactions can be explained by assuming the presence of a "rate-promoting" vibration which brings donor and acceptor closer together, thus leading to rate enhancement. There has never been a study of this effect for classical systems. We used transition path sampling to study the equivalent classical problem and found a complicated dynamical behavior that cannot be captured by transition state theory. Slow promoting vibrations lead to reactive trajectories that overshoot the saddle point, but on the other hand the short period of fast oscillations allows the reactants to stay only briefly in a low-barrier regime. There is a competition between these effects, which results to an intermediate value for the frequency of the rate-promoting vibration that is optimal for enhancing the rate.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.281
Teacher spread0.257 · 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

Citations35
Published2004
Admission routes1
Has abstractyes

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