A quasi-classical trajectory study of the reagent initial rotation quantum state effect on the reaction He + T<sub>2</sub><sup>+</sup>→ HeT<sup>+</sup> + T using an improved ab initio potential energy surface
Bibliographic record
Abstract
In this work, we employed the quasi-classical trajectory (QCT) method to study the vector correlations and the influence of the reagent initial rotational quantum number j for the reaction He + T2+(v = 0, j = 0–3) → HeT+ + T on a new potential energy surface (PES). The PES was improved by Aquilanti co-workers (Chem. Phys. Lett. 2009. 469: 26–30). The polarization-dependent differential cross sections (PDDCSs) and the distributions of P(θr), P([Formula: see text]r), and P(θr, [Formula: see text]r) are presented in this work. The plots of the PDDCSs provide us with abundant information about the distribution of the product angular momentum polarization. The P(θr) is used to describe the correlation between k (the relative velocity of the reagent) and j′ (the product rotational angular momentum). The distribution of dihedral angle P([Formula: see text]r) shows the k–k′–j′ (k′ refers to the relative velocity of the product) correlation. The PDDCS calculations illustrate that the product of this reaction is mainly backward scatter and it has the strongest polarization in the backward and sideways scattering directions. At the same time, the results of the P([Formula: see text]r) demonstrate that the product HeT+tends to be oriented along the positive direction of the y axis and it tends to rotate right-handedly in planes parallel to the scattering plane. Moreover, the distribution of the P(θr) manifests that the product angular momentum is aligned along different directions relative to k. The direction of the product alignment may be perpendicular, opposite, or parallel to k. Moreover, our calculations are independent of the initial rotational quantum number.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".