Challenges in predicting ΔrxnG in solution: The chelate effect
Bibliographic record
Abstract
Gibbs energies for reactions involving aqueous ions are challenging to predict due to the large solvation energies of such ions. A stringent test would be the ab initio reproduction of the aqueous-phase chelate effect, an entropic effect in reactions of very small enthalpy changes. This paper examines what is required to achieve such a reproduction for the paradigmatic reaction M(NH3)42+ + 2 en → M(en)22+ + 4 NH3 (en = 1,2-ethylenediamine), for which ΔrxnG* and ΔrxnH* are −2.3 and +1.6 kcal mol−1, respectively, if M = Zn. Explicit solvation via simulation was avoided in order to allow sufficiently accurate electronic structure models; this required the use of continuum solvation models (CSMs), and a great deal of effort was made in attempting to lower the relative errors of ΔsolvG*[M(NH3)42+] vs ΔsolvG*[M(en)22+] from the CSMs available in Gaussian software. CSMs in ADF and JDFTx software were also tested. A uniform 2.2 kcal mol−1 accuracy in ΔrxnG* for all three metal-atom choices M = {Zn, Cd, Hg} was eventually achieved, but not from any of the known CSMs tested, nor from cavity size reoptimization, nor from semicontinuum modeling: post facto solvation energy corrections [one per solute type, NH3, en, M(NH3)42+, M(en)22+] were needed. It is hoped that this study will aid (and encourage) further CSM development for coordination-complex ions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".