Semicontinuum (Cluster-Continuum) Modeling of Acid-Catalyzed Aqueous Reactions: Alkene Hydration
Bibliographic record
Abstract
An effort is made to reduce the errors of continuum solvation models (CSMs) with semicontinuum modeling to achieve 3 kcal mol –1 agreement with experiment for acid-catalysis activation Gibbs energies. First, two underappreciated CSM issues are reviewed: errors in the CSM solvation Gibbs energies grow beyond 5 kcal mol –1 (i) as ions are made smaller and (ii) as water clusters grow larger. Second, the computational reproduction of the known Gibbs energies (Δ r G and Δ ‡ G ) of the paradigmatic reaction ethene + H 2 O + H 3 O + → TS + → ethanol + H 3 O + is attempted. It is argued that, despite the >5 kcal mol –1 solvation errors for ions, it is possible to employ error cancellation strategies to reduce the errors in the reaction and activation Gibbs energies to 3 kcal mol –1 accuracy. A new 3 kcal mol –1 effect due to solvent-molecule “placement” (confinement from 1 M bulk concentration) was isolated and proved useful. Third, computational reproduction of the known entropies (Δ r S and Δ ‡ S ) of the paradigmatic reaction is attempted using Trouton’s constant and neglect of solvent structure reorganization effects (which must cancel well for this reaction); this worked well for Δ r S but needs empirical correction of ∼11 cal mol –1 K –1 for Δ ‡ S due to solvent disorientation when H 3 O + is consumed. These entropy estimates allow for enthalpy (Δ r H and Δ ‡ H ) estimation from the Gibbs energy values. Fourth, two recommended options, including A + H 3 O + ·2W → [AHOH 2 + ·2W] ‡, are shown to also work well for the activations of propene and isobutene.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".