Bonding reactivity descriptor from conceptual density functional theory and its applications to elucidate bonding formation
Bibliographic record
Abstract
Condensed-to-atom Fukui functions which reflect the atomic reactivity like the tendency susceptible to either nucleophilic or electrophilic attack demonstrate the bonding trend of an atom in a molecule. Accordingly, Fukui functions based concepts, that is, bonding reactivity descriptors which reveal the bonding properties of molecules in the reaction were put forward and then applied to pericyclic and cluster reactions to confirm their effectiveness and reliability. In terms of the results from the bonding descriptors, a covalent bond can readily be predicted between two atoms with large Fukui functions (i.e., one governs nucleophilic attack while the other one governs electrophilic attack, or both of them govern radical attacks) for pericyclic reactions. For SinOm clusters’ reactions, the clusters with a low O atom ratio readily form a bond between two Si atoms with big values of their Fukui functions in which they respectively govern nucleophilic and electrophilic attacks or both govern radical attacks. Also, our results from bonding descriptors show that Si—Si bonds can be formed via the radical mechanism between two Si atoms, and formations of Si—O and O—O bonds are possible when the O content is high. These results conform with experimental findings and can help experimentalists design appropriate clusters to synthesize Si nanowires with high yields. The approach established in this work could be generalized and applied to study reactivity properties for other systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".