“Adiabatic-hindered-rotor” treatment of the parahydrogen-water complex
Bibliographic record
Abstract
Inspired by a recent successful adiabatic-hindered-rotor treatment for parahydrogen pH(2) in CO(2)-H(2) complexes [H. Li, P.-N. Roy, and R. J. Le Roy, J. Chem. Phys. 133, 104305 (2010); H. Li, R. J. Le Roy, P.-N. Roy, and A. R. W. McKellar, Phys. Rev. Lett. 105, 133401 (2010)], we apply the same approximation to the more challenging H(2)O-H(2) system. This approximation reduces the dimension of the H(2)O-H(2) potential from 5D to 3D and greatly enhances the computational efficiency. The global minimum of the original 5D potential is missing from the adiabatic 3D potential for reasons based on solution of the hindered-rotor Schrödinger equation of the pH(2). Energies and wave functions of the discrete rovibrational levels of H(2)O-pH(2) complexes obtained from the adiabatic 3D potential are in good agreement with the results from calculations with the full 5D potential. This comparison validates our approximation, although it is a relatively cruder treatment for pH(2)-H(2)O than it is for pH(2)-CO(2). This adiabatic approximation makes large-scale simulations of H(2)O-pH(2) systems possible via a pairwise additive interaction model in which pH(2) is treated as a point-like particle. The poor performance of the diabatically spherical treatment of pH(2) rotation excludes the possibility of approximating pH(2) as a simple sphere in its interaction with H(2)O.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".