Interactions in Large, Polyaromatic Hydrocarbon Dimers: Application of Density Functional Theory with Dispersion Corrections
Bibliographic record
Abstract
The interactions within two models for graphene, coronene and hexabenzocoronene (HBC), and (H 3C(CH 2) 5) 6-HBC, a synthesizable model for asphaltenes, were studied using density functional theory (DFT) with dispersion corrections. The corrections were implemented using carbon atom-centered effective core-type potentials that were designed to correct the erroneous long-range behavior of several DFT methods. The potentials can be used with any computational chemistry program package that can handle standard effective core potential input, without the need for software modification. Testing on a set of common noncovalently bonded dimers shows that the potentials improve calculated binding energies by factors of 2-3 over those obtained without the potentials. Binding energies are predicted to within ca. 15%, and monomer separations to within ca. 0.1 A, of high-level wave function data. The application of the present approach predicts binding energies and structures of the coronene dimer that are in excellent agreement with the results of other DFT methods in which dispersion is taken into account. Dimers of HBC show extensive binding in pi-stacking arrangements, with the largest binding energy, 44.8 kcal/mol, obtained for a parallel-displaced structure. This structure is inline with the published crystal structure. Conformations in which the monomers are perpendicular to one another are much more weakly bound and have binding energies less than 10 kcal/mol. For dimers of (H 3C(CH 2) 5) 6-HBC, which contain 336 atoms, we find that a slipped-parallel structure with C s symmetry has a binding energy of 52.4 kcal/mol, 8.9 kcal/mol lower than that of a bowl-like, C 6 v -symmetric structure.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".