Towards understanding performance differences between approximate density functionals for spin states of iron complexes
Bibliographic record
Abstract
Density functional theory has been widely used to investigate the structural and electronic properties of heme-containing proteins such as cytochrome P450. Nevertheless, recent studies have shown that approximate exchange-correlation energy density functionals can incorrectly predict the stability order of spin states in, for instance, iron-containing pyridine and imidazole systems. This raises questions about the validity of earlier theoretical studies. In this work, we systematically investigate a few typical inorganic and organic iron-containing complexes and try to understand the performance difference of various density functionals. Two oxidation states of iron, Fe(II) and Fe(III), with different spin states and both adiabatic and vertical structures are considered. A different description of the outmost molecular orbital is found to play the crucial role. Local density and generalized gradient based functionals bias the lower spin state and produce a more localized frontier orbital that is higher in energy than the hybrid functionals. Energy component analysis has been performed, together with comparison of numerous structural and electronic properties. Implications of the present work to the theoretical study of heme-containing biological molecules and other spin-related systems are discussed.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".