Correlations between experimental and theoretical adiabatic ionization energies for organic compounds and rate constants for atmospheric reactions with hydroxyl radicals
Bibliographic record
Abstract
Abstract Adiabatic ionization energy (AIE) calculations were performed at the AM1, PM3, PM6, PDDG, HF/QZVP, and B3LYP/QZVP levels of theory on 722 atmospherically relevant organic compounds with available experimental rate constants for atmospheric reactions with hydroxyl radicals (k~OH~). From the starting set of molecules, a final suite of 114 mono- and polyfunctionalized compounds provided converged neutral and cationic geometries without imaginary frequencies for all six levels of theory. NIST evaluated AIEs were available for 54 compounds, providing mean absolute AIE prediction errors of 0.31 (AM1), 0.28 (PM3), 0.50 (PM6), 0.36 (PDDG), 1.22 (HF/QZVP), and 0.20 eV (B3LYP/QZVP). Modest correlations were found between the experimental (r=-0.68, SE=0.81) and computationally estimated (r=-0.77 [AM1], -0.75 [PM3], -0.83 [PM6], -0.79 [PDDG], -0.83 [HF/QZVP], and -0.82 [B3LYP/QZVP]; SE=0.75 [AM1], 0.78 [PM3], 0.66 [PM6], 0.73 [PDDG], 0.67 [HF/QZVP], and 0.68 [B3LYP/QZVP]) AIEs and the corresponding experimental log k~OH~. Univariate AIE versus k~OH~ correlations are of lower predictive ability than state-of-the-art multivariate techniques, and are limited by the inability to calculate reliable AIEs for large numbers of atmospherically relevant compounds due either to convergence failures at various levels of theory or the presence of imaginary frequencies for converged cationic geometries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".