Computational Study of Vertical Ionization Potentials Using Density Functional Theory and Green's Function Methods
Bibliographic record
Abstract
Abstract Over one hundred vertical ionization potentials (VIPs) were computed using density functional theory (DFT) and Green's function (GF) based methods. The DFT approaches include the unrestricted transition state (uTS) and unrestricted diffuse ionization (uDI) approximations using the Becke88‐Perdew86 exchange‐correlation functional. Green's function methods include the outer‐valence GF (OVGF) approach, the parametrized GF2 (pGF2), and the parametrized GF2 times screened interaction (pGW2) approximations. DFT computations of IPs using the uTS approximation was found to be nearly as accurate as those predicted using the elaborate OVGF method. The much more computationally efficient uDI approximation provides predictions of moderate accuracy and is recommended for computing IPs for larger molecules. We have observed that the average absolute deviations from a uDI calculation using poorer basis set (DZVP) and poorer geometry (AM1 optimization) is only slightly larger.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".