Importance of the Inclusion of Dispersion in the Modeling of Asphaltene Dimers
Bibliographic record
Abstract
Modeling of asphaltenes presents many challenges, not least of which is their large size and the lack of definitive experimental structure information. However, a further fundamental issue of importance is the ability of the modeling methodology to accurately predict noncovalent interactions, particularly dispersion (London) forces. The self-aggregation properties of asphaltenes are primarily driven by such interactions. Therefore, for a modeling approach to be insightful, dispersion must be accounted for. This point is illustrated in this work by examining the effect of dispersion on the conformer distribution for a series of asphaltene model dimers, including perylene bisimide-type molecules and for a model of Maya asphaltene. Inclusion of dispersion using dispersion-correcting potentials on standard density-functionals is shown to be critical to both structure and conformer population for these noncovalently bound dimers. For the Maya asphaltene, a previously postulated “open” structure is shown to be ca. 9 kcal/mol less stable than a conformer that allows for greater π−π overlap within its central archipelago-type moiety. This finding is in line with recent NMR work, which indicates that “closed” structures dominate for asphaltenes. N, N ′-dimethyl-perylene bisimide dimer has a binding energy (BE) of up to 29 kcal/mol, while the more complex N, N ′-(1-hexylheptyl)-perylene bisimide model is slightly less strongly bound (BE = 25 kcal/mol) since the large alkyl substituents restrict the ability of the extensive π-regions to overlap. Such conclusions cannot be drawn when methods that do not incorporate dispersion, e.g., the B3LYP density-functional, are used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".