Simultaneous Determination of Structures, Vibrations, and Frontier Orbital Energies from a Self-Consistent Range-Separated Hybrid Functional
Bibliographic record
Abstract
A self-consistent optimally tuned range-separated hybrid density functional (scOT-RSH) approach is developed. It can simultaneously predict accurate geometries, vibrational modes, and frontier orbital energies. This is achieved by optimizing the range-separation parameter, γ, to both satisfy the ionization energy theorem and minimize interatomic forces. We benchmark our approach against an established hybrid functional, B3LYP, using the G2 test set. scOT-RSH greatly improves the accuracy of occupied frontier orbital energies, with a mean absolute error (MAE) of only 0.2 eV relative to experimental ionization energies compared to 2.96 eV with B3LYP. Geometries do not change significantly compared to those obtained from B3LYP, with a bond length MAE of 0.012 Å compared to 0.008 Å for B3LYP, and a 6.5% MAE for zero-point energies, slightly larger than that of B3LYP (3.1%). scOT-RSH represents a new paradigm in which accurate geometries and ionization energies can be predicted simultaneously from a single functional approach.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".