Performance assessment of density functional theory-based models using orbital momentum distributions
Bibliographic record
Abstract
The performance of a number of established and widely used density functional theory (DFT) methods (B3LYP, Bhandh, BP86, PW91, Vosko, Wilk, and Nusair (VWN), LB94, PBe0, statistical average of orbital potentials (SAOP) and X3LYP) and the Hartree–Fock (HF) method has been assessed using the 7σ orbital momentum distributions (MDs) of nitrous oxide (N2O). The DFT methods are combined with a number of Gaussian basis sets as well as even-tempered Slater basis sets. Orbital MDs of N2O are compared with experimental measurement of the same orbital from electron momentum spectroscopy. This study reveals information regarding the performance of (a) the DFT methods and their long-range behaviour, (b) Gaussian and Slater basis set contributions to this orbital, (c) combinations (i.e. the models) of the DFT methods and basis sets and (d) the entire region of chemical significance of the orbital. In general, for any given method, it is found that Slater basis sets achieve better overall agreement with experiment than Gaussian basis sets. Dunning's aug-cc-pVTZ basis set achieves the best performance for the MDs for this orbital among the Gaussian basis sets. The B3LYP and BP86 models exhibit similar overall performance to more recent models such as X3LYP and SAOP. This study also demonstrates that the particular combination of density functional method and basis set employed have a significant effect on the quality of the calculated orbitals.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".