Do we need delocalised wavefunctions for the excited state dynamics of 1,1-difluoroethylene?
Bibliographic record
Abstract
In this work, we set up a model Hamiltonian to study the excited state quantum dynamics of 1,1-difluoroethylene, a molecule that has equivalent atoms exchanged by a torsional symmetry operation leading to equivalent minima on the potential energy surface. In systems with many degrees of freedom where the minimum energy geometry is not unique, the ground state wavefunction will be delocalised among multiple minima. In this small test system, we probe the excited state dynamics considering localised (in a single minimum) and delocalised (spread over among multiple minima) wavefunctions and check whether this choice would influence the final outcome of the quantum dynamics calculations. Our molecular Hamiltonian comprises seven electronic states, including valence and Rydberg states, computed with the MS-CASPT2 method and projected onto the vibrational coordinates of the twelve normal modes of 1,1-difluoroethylene in its vibrational ground state. This Hamiltonian has been symmetrised along the torsional degree of freedom to make both minima completely equivalent and the model is supported by the excellent agreement with the experimental absorption spectrum. Quantum dynamics results show that the different initial conditions studied do not appreciably affect the excited state populations or the absorption spectrum when the dynamics is simulated assuming a delta pulse excitation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".