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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".