van der Waals potential energy surfaces from the exchange-hole dipole moment dispersion model
Bibliographic record
Abstract
The potential energy surfaces (PESs) of 28 simple van der Waals complexes, each consisting of a rare-gas (Rg) atom interacting with a linear molecule, are calculated using the exchange-hole dipole moment (XDM) dispersion model in conjunction with three base density functionals (HFPBE, PW86PBE, and a commensurate hybrid functional). Results are compared with literature coupled-cluster reference data. The quality of the computed PESs is assessed based on the positions of the global minima and the corresponding binding energies. Only the hybrid functional is found to provide generally reliable PESs. Dispersion-corrected HFPBE strongly underestimates the equilibrium intermolecular separations and predicts different global minima than the reference PESs for Rg–HCl, Rg–HBr, and two of the Rg–HCN complexes. Analysis of the binding-energy errors reveals that the performance of HFPBE degrades as the size of the Rg atoms increase down the group, while the performance of PW86PBE is significantly worse for strongly-polar molecules. PW86PBE, and to a lesser extent the hybrid, strongly overbind Kr–HF due to charge-transfer error. Despite this, the XDM-corrected hybrid functional displays the best overall error statistics and provides binding energies to within ca. 10 cm–1 of the coupled-cluster reference data at a greatly reduced computational cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".