Bibliographic record
Abstract
We develop a non-linear and non-empirical (nlane) double hybrid density functional derived from an accurate interpolation of the adiabatic connection in density functional theory, incorporating the correct asymptotic expansions. By bridging the second-order perturbative weak correlation limit with the fully interacting limit from the semi-local SCAN functional, nlane-SCAN is free of fitted parameters while providing improved energetic predictions compared to SCAN for moderately and strongly correlated systems alike. It delivers accurate predictions for atomic total energies and multiple reaction datasets from the GMTKN55 benchmark while significantly outperforming traditional linear hybrids and double hybrids for non-covalent interactions without requiring dispersion corrections. Due to the exact constraints at the weak correlation limit, nlane-SCAN has reduced delocalization errors, as evident through the SIE4x4 benchmark and bond dissociations of H2+ and He2+. Its proper asymptotic behavior ensures stability in strongly correlated systems, improving H2 and N2 bond dissociation profiles compared to conventional functionals.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".