Asymptotic Pairwise Dispersion Corrections Can Describe Layered Materials Accurately
Bibliographic record
Abstract
2018, 2, 034005] found that few density functionals, none of which are asymptotic pairwise dispersion methods, describe the geometry and binding of layered materials accurately. Here, we show that the exchange-hole dipole moment (XDM) dispersion model attains excellent results for graphite, hexagonal BN, and transition-metal dichalcogenides. Contrary to what has been argued, successful modeling of layered materials does not necessitate meta-GGA exchange, nonlocal correlation functionals, or the inclusion of three-body dispersion terms. Rather, a GGA functional, combined with a simple asymptotic pairwise dispersion correction, can be reliably used, provided that it properly accounts for the geometric dependence of the dispersion coefficients. The overwhelming contribution to the variation of the pairwise dispersion coefficients comes from the immediate vicinity of an atom and is already present for single layers. Longer-range and interlayer effects are examined in detail for graphite.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".