Performance of conventional and dispersion-corrected density-functional theory methods for hydrogen bonding interaction energies
Bibliographic record
Abstract
The approximate CCSD(T)/CBS binding energies for the set of 23 hydrogen-bonded dimers (HB23) of the S66 set reported by Řezáč et al. (J. Chem. Theory Comput. 2011, 7, 2427-2438) were expected to be under-estimated based on the known under-binding tendency of the counterpoise correction combined with small basis sets. In this work, we present binding energies for the HB23 set of dimers obtained using a composite approach recently described by Mackie and DiLabio (J. Chem. Phys. 2011, 135, 134318) that averages the counterpoise- and non-counterpoise-corrected energies, while utilizing standard approaches to obtain CCSD(T)/CBS-type energies. The binding energies for the HB23 set are revised upward by an average of 0.12 kcal mol(-1) and as much as 0.35 kcal mol(-1). We use these improved benchmark-level binding energies to evaluate the ability of pure, hybrid, long-range-corrected, and dispersion-corrected density-functional theory (DFT) methods to accurately predict the binding energies of hydrogen-bonded dimers. We find that, in general, the inclusion of dispersion into the DFT approach is required in order to obtain reasonable results for the HB23 set. We find that the dispersion-corrected DFT methods we tested produce results of variable quality, as measured by mean absolute deviation relative to the revised reference values we computed: B97D, 0.57 kcal mol(-1); B3LYP-D3, 0.44 kcal mol(-1); ωB97XD, 0.25 kcal mol(-1); LC-ωPBE-D3, 0.24 kcal mol(-1); M06-2X, 0.21 kcal mol(-1); B3LYP-DCP, 0.23 kcal mol(-1); B971-XDM, 0.18 kcal mol(-1).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".