Dispersion XDM with Hybrid Functionals: Delocalization Error and Halogen Bonding in Molecular Crystals
Bibliographic record
Abstract
The accurate calculation of relative lattice energies of molecular crystals is important in polymorph ranking and crystal structure prediction. Delocalization error has been shown to affect calculated intermolecular binding energies in DFT and is similarly expected to affect the lattice energies of some classes of molecular crystals. In this work, we explore the use of dispersion-corrected hybrid functionals in the planewave-pseudopotentials approach to reduce delocalization error. We combine several hybrid functionals with the exchange-hole dipole moment (XDM) model for dispersion and show that they generally outperform GGA functionals in the calculation of both gas-phase binding energies and molecular crystal lattice energies. We apply the resulting XDM-corrected functionals to four halogen-bonded crystals: Cl 2, Br 2, I 2, and ICl. GGA functionals severely overestimate their lattice energies, while hybrid functionals give accurate values. The preference of GGA functionals for monatomic structures in the Br 2 and Cl 2 crystals is also explained. Finally, we apply a recently developed method to calculate Bader’s delocalization indices to examine the extent of intermolecular delocalization in the halogen molecular crystals. It is shown that intermolecular delocalization indices can be used to measure the strength of halogen bonds within the crystal, as well as detect the presence of delocalization error.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".