An Efficient Workflow for Generation of Conformational Ensembles of Density Functional Theory Quality: Dimers of Polycyclic (Hetero-)Aromatics
Bibliographic record
Abstract
Several composite density functional theory (DFT) methods, namely HF-3c, B97- 3c, PBEh-3c, r2SCAN-3c, and ωB97X-3c, were tested for accuracy and efficiency in computing binding energies of seven low-lying conformers of the pyrene homodimer, both in the gas phase and in toluene solution. The most promising method was B97-3c, with a Mean Absolute Deviation (MAD) for binding energies of 0.5 kJ/mol, relative to ωB97X-V/def2-TZVP results. Thus, B97-3c was used in a multi-tiered approach for generating conformational ensembles for a series of homodimers. The workflow involves six steps: (i) generate an initial ensemble, using the conformer-rotamer ensemble sam- pling tool (CREST), and its underlying GFN2-xTB method; (ii) reoptimize each mem- ber of the ensemble using B97-3c; (iii) discard duplicates and high-energy conformers; (iv) reoptimize the remaining conformers using ωB97X-D4/def2-SVP; (v) if needed, discard any high energy or duplicate conformers; (vi) compute vibrational frequencies using ωB97X-D4/def2-SVP and final single point energies using ωB97X-V/def2- QZVPP. The six-step workflow allows the generation of large DFT-quality ensembles efficiently, as demonstrated on the known pyrene dimer ensemble, and then applied to the homodimers of eight small polycyclic (hetero-)aromatic molecules related to asphaltenes: anthracene, phenanthrene, fluorenone, dibenzofuran, dibenzothiophene, dibenzothiophene oxide, N -methylcarbazole, and benzo[h]quinoline. The refined ensembles enabled an analysis of trends in dimerization structures and energies for these monomers, revealing a strong dependence for binding energy upon the magnitude of dipole cancellation.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".