Challenges in predicting Δ<sub>rxn</sub><i>G</i> in solution: Hydronium, hydroxide, and water autoionization
Bibliographic record
Abstract
Abstract Standard non‐semiempirical continuum‐dielectric orbital‐based methods horribly overpredict, by 26‐50 kcal mol−1, the Gibbs energy for the water autoionization reaction 2 H2O(l) → H3O+(aq) + OH–(aq). Here, we demonstrate these errors, fully investigate the reasons for these errors, and show that the use of 4 explicit solvent within the continuum (the “semicontinuum,” “cluster‐continuum,” or “hybrid” technique) can reduce the error of a standard continuum model from 50 to 2 kcal mol−1. Results from pure cluster, pure continuum (several versions including semiempirical ones), and semicontinuum modeling are each presented and discussed. We recommend use of 3 waters around hydronium and 4 waters around hydroxide with standard continua whenever these ions are involved in reaction. To the possible surprise of some, time‐consuming molecular‐dynamics simulations are not needed to reproduce this problematic energy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".