Dioxygen binding to Fe-MOF-74: microscopic insights from periodic QM/MM calculations
Bibliographic record
Abstract
Accurate MOF-FF parameter sets were determined for the ferrous and ferric forms of an iron-based metal–organic framework (MOF) called Fe-MOF-74. For this purpose, density functional theory (DFT) calculations were applied to truncated cluster models of Fe-MOF-74, and the DFT-calculated geometries and energy derivatives were used for the force-field parameterization. The resultant parameter sets performed remarkably well in reproducing the experimentally determined structure of the MOF. We also performed periodic quantum mechanics (QM) / molecular mechanics (MM) calculations employing a subtractive scheme called ONIOM, with the optimized MOF-FF parameters used for the MM calculations, in an attempt to evaluate the binding energies between O2 and several Fe-MOF-74 variants. The calculated binding energy for Fe-MOF-74 agreed very well with the experimental value, and QM/MM geometry optimization calculations confirmed that the O2-bound complex has a side-on geometry. Our calculations also predicted that, when the two neighboring iron ions around the O2-binding site are replaced with other metal ions (Mg2+, Ni2+, Zn2+, Co2+, or Mn2+), there are noticeable variations in the binding energy, indicating that these substituted metal ions affect the O2 binding indirectly.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".