Ground and low‐lying excited <i>C</i><sub>2<i>v</i></sub> states of FeO<sub>2</sub>—A challenge to computational methods
Bibliographic record
Abstract
Abstract DFT (BPW91 and B3PW91), CCSD, CCSD(T), and MP2 geometry optimizations were performed on the lowest A 1 , A 2 , B 1 , and B 2 singlet to septet states (a total of 16 states) of FeO 2 , using in most cases the 6‐311+G(3df) basis set. With few exceptions, the different methods led to similar geometries. The lowest state is 3 B 1 or 5 B 2 , depending on the method used. The quality of the various computational methods was tested by a comparison of vertical excitation energies with corresponding high‐level MRCI values. The best agreement with MRCI was found for BPW91 and B3PW91, with average deviations of about 0.2 eV. MRCI calculations were carried to extremely low configuration selection thresholds (0.025 μh) in order to find the lowest state of FeO 2 . The full CI estimate (including Davidson correction and energy extrapolation to 0 μh) gave 3 A 1 as the lowest state, followed by 5 B 2 (0.03 eV) and 3 B 1 (0.06 eV). Both 3 B 1 and 3 A 1 have geometries and vibrational frequencies consistent with the observed IR spectrum. The calculated adiabatic electron affinity of FeO 2 (from 3 B 1 of FeO 2 to 4 B 2 of FeO ) is 2.46 eV with CCSD(T), and 2.22 eV with BPW91, compared with the experimental value of 2.36 eV. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2009
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".