Density Functional Studies of Iron-Porphyrin Cation with Small Ligands X (X: O, CO, NO, O<sub>2</sub>, N<sub>2</sub>, H<sub>2</sub>O, N<sub>2</sub>O, CO<sub>2</sub>)
Bibliographic record
Abstract
Molecular structure of the iron porphyrin cation [FeP]+ with small ligands X (X: O, CO, NO, O2, N2, H2O, N2O, CO2) are studied employing density functional theory (DFT) methods with the exchange-correlation (XC) functionals OPBE and B3LYP using the LANL2DZ basis set. The relative spin state energies and bond dissociation energies of all of the complexes are presented at their optimized geometries. The low-spin (S = 1/2, S = 0) state is found to be the lowest energy states for the [FePO]+, [FePCO]+, and [FePNO]+ complexes whereas the high-spin (S = 5/2) state has the lowest energy for the [FePO2]+ complex. The intermediate-spin (S = 3/2) state is found to be the lowest energy states for the [FePN2]+, [FePH2O]+, [FePN2O]+, and [FePCO2]+ complexes which exhibit the same relative spin-state energy ordering: (S = 3/2) < (S = 5/2) < (S = 1/2) as isolated [FeP]+, and the Fe-ligand bonding is very weak. The calculated bond dissociation energy using the OPBE XC-functional method has shown the following order for the lowest energy spin state: N2O < CO2 < N2 < O2 < H2O < CO < NO < O. This level of theory was previously shown to be the only DFT method capable of correctly predicting the spin ground state of iron compounds, and we find similar good performance of OPBE XC-functional in the current study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".