Dispersion Corrections Improve the Accuracy of Both Noncovalent and Covalent Interactions Energies Predicted by a Density-Functional Theory Approximation
Bibliographic record
Abstract
The use of pairwise dispersion corrections together with dispersion-correcting potentials (DCPs) offers a computationally low-cost approach to improving the performance of a density-functional theory based method with respect to the prediction of important chemical properties. In this work, we develop DCPs for the C, H, N, and O atoms for use with the BLYP generalized gradient approximation functional coupled with "D3" pairwise dispersion corrections and 6-31+G(2d,2p) basis sets. The combined approach, referred to as BLYP-D3-DCP, offers generally improved performance over both unadorned BLYP and BLYP with D3 corrections with respect to the prediction of noncovalent binding energies (BEs) and covalent bond dissociation enthalpies (BDEs). Predicted barrier heights for a set of pericyclic and Diels-Alder reactions are improved in some instances, as are organic bond separation reaction energies and radical stabilization energies. It is also shown that the BLYP-D3-DCP approach outperforms B3LYP-D3 in the prediction of many chemical properties, in particular noncovalent BEs and BDEs, suggesting that the addition of D3 and DCP corrections, which have negligible computational cost, to simple density functionals like BLYP may elevate their performance to that of more complex functionals such as B3LYP.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".