Enhancing the utility of <sup>1</sup><i>J</i><sub>CH</sub> coupling constants in structural studies through optimized DFT analysis
Bibliographic record
Abstract
Commonly used DFT methods for the calculation of 1JCH coupling constants have typically required the application of ad hoc correction factors, modification of functionals, or empirical scaling to improve the fit between predicted and experimental values. Here we demonstrate that highly accurate 1JCH coupling predictions can be obtained without such adjustments by careful selection of DFT methods for geometry optimization and J-coupling calculations (e.g. B3LYP/6-31G(d,p)(mixed)//mPW1PW/cc-pVTZ). The proposed method was cross-validated against a diverse set of 122 1JCH couplings and was successfully applied to the conformational and stereochemical analysis of strychnine and a previously unreported trachylobane diterpene natural product.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".