Effects of Spin–Orbit Coupling on Covalent Bonding and the Jahn–Teller Effect Are Revealed with the Natural Language of Spinors
Bibliographic record
Abstract
The orbital-based natural language describing the complexity of chemistry ( Stowasser , R. ; Hoffmann , R. J. Am. Chem. Soc. 1999 , 121 , 3414 ) was extended by us recently to the definition of spin-orbit natural spinors ( Zeng , T. et al. J. Chem. Phys. 2011 , 134 , 214107 ). This novel method gives chemical insights into the role of spin-orbit coupling in covalent bonding and in the Jahn-Teller effect. The natural spinors are used to explain antibonding spin-orbit effects on TlH and Tl2: it is found that the spin-orbit induced charge transfer from the bonding to the nonbonding or antibonding orbitals has a large effect on the bond strength. The natural spinors are also used to explain the spin-orbit quenching of the Jahn-Teller effect in WF5: the spin-orbit interaction can stabilize the totally symmetric electron distribution so that the high-symmetry molecular structure becomes more stable than its distortions. A general discussion of the role of the spin-orbit coupling in covalent bonding and Jahn-Teller effect is given in terms of the competition between the rotational nature of the spin-orbit coupling and the directionality of the two effects. The natural spinors offer the advantage of providing a simple and clear pictorial explanation for the profound relativistic spin-dependent interactions in chemistry often appearing as a black box answer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".