Many-body dispersion in model systems and the sensitivity of self-consistent screening
Bibliographic record
Abstract
London dispersion is a weak, attractive, intermolecular force that occurs due to interactions between instantaneous dipole moments. While individual dispersion contributions are small, they are the dominating attractive force between nonpolar species and determine many properties of interest. Standard semi-local and hybrid methods in density-functional theory do not account for dispersion contributions, so a correction such as the exchange-hole dipole moment (XDM) or many-body dispersion (MBD) models must be added. Recent literature has discussed the importance of many-body effects on dispersion, and attention has turned to which methods accurately capture them. By studying systems of interacting quantum harmonic oscillators from first principles, we directly compare computed dispersion coefficients and energies from XDM and MBD and also study the influence of changing oscillator frequency. Additionally, the 3-body energy contributions for both XDM, via the Axilrod-Teller-Muto term, and MBD, via a random-phase approximation formalism, are calculated and compared. Connections are made to interactions between noble gas atoms as well as to the methane and benzene dimers and to two layered materials, graphite and MoS2. While XDM and MBD give similar results for large separations, some variants of MBD are found to be susceptible to a polarization catastrophe at short range, and the MBD energy calculation is seen to fail in some chemical systems. Additionally, the self-consistent screening formalism used in MBD is shown to be surprisingly sensitive to the choice of input polarizabilities.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".