Perturbative wave-packet spawning procedure for non-adiabatic dynamics in diabatic representation
Bibliographic record
Abstract
I present a new formulation of wave-packet spawning procedure based on a second order perturbation theory expression for population transfer between different diabatic electronic states. The employed perturbation theory (PT) expansion is based on an assumption that diabatic states can be represented locally with their Taylor series up to quadratic terms in nuclear coordinates (local harmonic approximation). The corresponding local harmonic basis of vibrational states makes infinite summation over excited states in PT expressions possible, and thus, it provides a complete basis set expression for the population transfer. This allows me to detect when a finite basis set expansion employed in variational wave packet propagation does not adequately describe the interstate population transfer. Also, it suggests a rigorous criterion for basis set expansion (spawning). The proposed procedure is illustrated for the variational multiconfigurational Gaussian wave packet method applied to 1D and 2D model examples, and it also can be extended to direct on-the-fly dynamics with any Gaussian wave packet propagation method.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".