Evaluating Force-Field London Dispersion Coefficients Using the Exchange-Hole Dipole Moment Model
Bibliographic record
Abstract
The exchange-hole dipole moment (XDM) model from density-functional theory predicts atomic and molecular London dispersion coefficients from first principles, providing an innovative strategy to validate the dispersion terms of molecular-mechanical force fields. In this work, the XDM model was used to obtain the London dispersion coefficients of 88 organic molecules relevant to biochemistry and pharmaceutical chemistry and the values compared with those derived from the Lennard-Jones parameters of the CGenFF, GAFF, OPLS, and Drude polarizable force fields…..(see full abstract). Finally, XDM-derived dispersion coefficients were used to parameterize molecular-mechanical force fields for five liquids – benzene, toluene, cyclohexane, n-pentane, and n-hexane – which resulted in improved accuracy in the computed enthalpies of vaporization despite only having to evaluate a much smaller section of the parameter space.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".