Rate constants for decarboxylation reactions calculated using no barrier theory
Bibliographic record
Abstract
No barrier theory (NBT) provides both a qualitative way of thinking about what makes a reaction fast or slow and a quantitative way of calculating the rate constant (free energy of activation) corresponding to a particular mechanism. The origin and development of this idea are reviewed and examples of its use for qualitative understanding are presented before applying it to a set of decarboxylations. From the literature, a set of best values for rate constants for decarboxylation was picked. Detailed mechanistic models were developed for reactions leading to delocalized “anions” or to localized anions. It was necessary to have pKa values for ionizaion of the carbon acids corresponding to all of these species and these were selected from the literature or estimated by linear free energy relations (or occasionally calculated from proton exchange data). Over the entire range of measured decarboxylation rate constants, a range of 1025 in rate constant, the calculated values were in good agreement with experiment, with two exceptions: malonate dianion, which has been reported but probably not measured, and glycine, where it is possible that a different mechanism is being followed, unfortunately, one which we do not yet know how to treat by NBT. NBT is both a qualitatively and quantitatively useful tool for understanding chemistry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".