Accurate analytic He–H2 potential energy surface from a greatly expanded set of <i>ab initio</i> energies
Bibliographic record
Abstract
The interaction potential energy surface (PES) of He–H2 is of great importance for quantum chemistry, as the simplest test case for interactions between a molecule and a closed-shell atom. It is also required for a detailed understanding of certain astrophysical processes—namely, collisional excitation and dissociation of H2 in molecular clouds—at densities too low to be accessible experimentally. A new set of 23 703 ab initio energies was computed for He–H2 geometries where the interaction energy was expected to be non-negligible. These have an estimated rms “random” error of ∼0.2 mhartree and a systematic error of ∼0.6 mhartree (0.4 kcal/mol). A new analytic He–H2 PES, with 112 parameters, was fitted to 20 203 of these new ab initio energies (and to an additional 4862 points generated at large separations). This yielded an improvement by better than an order of magnitude in the fit to the interaction region, relative to the best previous surfaces (which were accurate only for near-equilibrium H2 molecule sizes). This new PES has an rms error of 0.95 mhartree (0.60 kcal/mol) relative to the 14 585 ab initio energies that lie below twice the H2 dissociation energy and 2.97 mhartree (1.87 kcal/mol) relative to the full set of 20 203 ab initio energies (the fitting procedure used a reduced weight for high energies, yielding a weighted rms error of 1.42 mhartree—i.e., 0.89 kcal/mol). These rms errors are comparable to the estimated error in the ab initio energies themselves; the conical intersection between the ground state and the first excited state is the largest source of error in the PES.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".