Evaluating dispersion forces for optimization of van der Waals complexes using a non-empirical functional
Bibliographic record
Abstract
Modelling dispersion interactions with traditional density functional theory (DFT) is a challenge that has been extensively addressed in the past decade. The exchange-dipole moment (XDM), among others, is a non-empirical add-on dispersion correction model in DFT. The functional PW86+PBE+XDM for exchange, correlation and dispersion, respectively, compromises an accurate functional for thermochemistry and for van der Waals (vdW) complexes at equilibrium and non-equilibrium geometries. To use this functional in optimizing vdW complexes, rather than computing single point energies, it is necessary to evaluate accurate forces. The purpose of this paper is to validate that, along the potential energy surface, the distance at which the energy is minimum is commensurate with the distance at which the forces vanish to zero. This test was validated for 10 rare gas diatomic molecules using various integration grids and different convergence criteria. It was found that the use of either convergence criterion, 10 −6 or 10 −8 , in Gaussian09, does not affect the accuracy of computed optimal distances and binding energies. An ultra-fine grid needs to be used when computing accurate energies using generalized gradient approximation functionals. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".