An Efficient <i>a Posteriori</i> Treatment for Dispersion Interaction in Density-Functional-Based Tight Binding
Bibliographic record
Abstract
The performance of density functional theory (DFT) (VWN-LDA, PBE-GGA, and B3LYP hybrid functionals), density-functional-based tight binding (DFTB), and ab initio methods [HF, MP2, CCSD, and CCSD(T)] for the treatment of London dispersion is investigated. Although highly correlated ab initio methods are capable of describing this phenomenon, if they are used with rather large basis sets, DFT methods are found to be inadequate for the description of H2/PAH (polycyclic aromatic hydrocarbon) interactions. As an alternative approach, an a posteriori addition of a van der Waals term to DFTB is proposed. This method provides results for H2/PAH interactions in close agreement with MP2 and higher-level ab initio methods. Bulk properties of graphite also compare well with the experimental data.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".