Perspective on London’s dispersion interaction
Bibliographic record
Abstract
Dispersion is a ubiquitous intermolecular force that affects short- and long-range potentials between molecules. Dispersion affects crystallisation, self-assembly, enzyme selectivity, and surface interactions, among countless other processes. Hence, it is of interest to be able to quantify dispersion when considering intermolecular potentials using quantum chemical techniques. This communication presents a first-principles method for computing the dispersion interaction quantum chemically. London’s description of dispersion is strictly only valid at large intermolecular separations since it is predicated on the free mutual rotation of molecules. We recast the description of London’s dispersion interaction in modern terminology and show that dispersion is caused by the correlation of electrons on different molecules and is hence attractive at all intermolecular separations. The discussion below extends the description of dispersion to all separations and orientations. This permits computation of instantaneous dispersion in systems with constrained mutual rotation and the formation of dispersion aggregates. We also show that ab initio correlated methods include dispersion effects naturally and discuss the balanced treatment of dispersion using these techniques. Finally, we propose a natural extension to the method to include quadrupolar and octupolar dispersion interactions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".