Accuracy of recent potential energy surfaces for the He–N2 interaction. II. Molecular beam scattering and bulk gas relaxation phenomena
Bibliographic record
Abstract
A new semiempirical exchange-Coulomb model potential energy surface for the N(2)-He interaction was reported recently [A. K. Dham et al., J. Chem. Phys. 127, 054302 (2007)] and, using it, the temperature dependence of bulk gas properties of N(2)-He mixtures, such as the second virial coefficient and traditional transport phenomena, most of which depend primarily on the isotropic component of the interaction potential energy surface, was determined. Values of these properties, along with values calculated using two high-quality ab initio potential energy surfaces [C.-H. Hu and A. J. Thakkar, J. Chem. Phys. 104, 2541 (1996); K. Patel et al., ibid 119, 909 (2003)] were compared critically to available experimental data. The present paper reports on the ability of the same three potential energy surfaces to predict state-to-state and total differential cross sections, total integral cross sections, and the temperature dependence of bulk gas relaxation phenomena (including magnetic field effects on transport coefficients). While all three potential energy surfaces give total differential and higher speed integral scattering results that fall within the experimental uncertainties, integral scattering results and state-to-state differential cross section measurements consistently exceed the calculated values. All three surfaces give similar agreement with the relaxation properties of N(2)-He binary mixtures, with the semiempirical exchange-Coulomb model potential energy surface giving slightly better overall agreement with experiment than the two ab initio potential energy surfaces.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".