Assessment of the PW86+PBE+XDM density functional on van der Waals complexes at non-equilibrium geometries
Bibliographic record
Abstract
The deficiency of conventional density-functional theory (DFT) in properly describing van der Waals (vdW) (especially dispersion-bound) complexes has been extensively addressed in the past decade. There are now several new methods published in the literature that are capable of accurately capturing weak dispersion interactions in complexes at equilibrium geometries. However, the performance of these new methods at non-equilibrium geometries remains to be assessed. We have previously published [F. O. Kannemann and A. D. Becke, J. Chem. Theory Comput. 6, 1081 (2010); A. D. Becke, A. A. Arabi, and F. O. Kannemann, Can. J. Chem. 88, 1057 (2010)] that the functional PW86+PBE+XDM for exchange + correlation + dispersion, respectively, is a highly accurate functional for general thermochemistry and vdW complexes at equilibrium geometries. Here, we show that this nonempirical, except for two parameters in the dispersion damping part, functional also performs well for vdW complexes at compressed and stretched intermonomer separations. The mean absolute relative error (MARE) is 9.4% overall for vdW complexes in the "S22×5" database incorporating compressed and stretched geometries [J. Rezac, K. E. Riley, and P. Hobza, J. Chem. Theory Comput. 7, 2427 (2011)]. Our largest MARE on the S22×5 database is 13.3% on the compressed geometry set.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".