Surface Adsorption from the Exchange-Hole Dipole Moment Dispersion Model
Bibliographic record
Abstract
The accurate calculation of intermolecular interaction energies with density functional theory requires methods that include a treatment of long-range, nonlocal dispersion correlation. In this work, we explore the ability of the exchange-hole dipole moment (XDM) dispersion correction to model molecular surface adsorption. Adsorption energies are calculated for six small aromatic molecules (benzene, furan, pyridine, thiophene, thiophenol, and benzenediamine) and the four DNA nucleobases (adenine, thymine, guanine, and cytosine) on the (111) surfaces of the three coinage metals (copper, silver, and gold). For benzene, where the experimental reference data is most precise, the mean absolute error in the computed absorption energies is 0.04 eV. For the other aromatic molecules, the computed binding energies are found to be within 0.09 eV of the available reference data, on average, which is well below the expected experimental uncertainties for temperature-programmed desorption measurements. Unlike other dispersion-corrected functionals, adequate performance does not require changes to the canonical XDM implementation, and the good performance of XDM is explained in terms of the behavior of the exchange hole. Additionally, the base functional employed (B86bPBE) is also optimal for molecular studies, making B86bPBE-XDM an excellent candidate for studying chemistry on material surfaces. Finally, the noncovalent interaction (NCI) plot technique is shown to detect adsorption effects in real space on the order of tenths of an eV.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".