Kohn–Sham density-functional study of the adsorption of acetylene and vinylidene on iron clusters, Fen/Fen+ (n=1–4)
Bibliographic record
Abstract
This is the first paper in a series dealing with the formation of benzene from acetylene on iron clusters, Fen/Fen+ (n=1–4). In the present study, we have performed all-electron Kohn–Sham density-functional theory calculations on the adsorption of acetylene and vinylidene on small iron clusters. Many starting structures were fully optimized without geometric and symmetric constraints for at least three different spin states (numbers of unpaired electrons) using gradient corrected functionals. Vibrational analyses have been performed on all the optimized structures. There is a large number of low-lying electronic states within a window of 50 kJ/mol above the lowest-energy structure for each cluster size and charge state. Various types of coordination and numbers of unpaired electrons are encountered in these electronic states. According to our energetic error bar, all of these states are possible candidates for the ground state of a given complex. Inclusion of corrections beyond the gradient of the density in generalized gradient approximation functionals for correlation stabilizes electronic states with high magnetic moment and destabilizes the low spin states. Electronic states corresponding to the adsorption of an acetylene or a vinylidene molecule on only one iron atom are also more stable when higher corrections are included in the correlation functional. Finally, we have excluded the participation of the vinylidene molecule in the reaction mechanism of the formation of benzene from acetylene on small iron clusters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".