Mixed Quantum-Classical Simulations of Transient Absorption Pump–Probe Signals for a Photo-Induced Electron Transfer Reaction Coupled to an Inner-Sphere Vibrational Mode
Bibliographic record
Abstract
In a previous study (Martinez, F.; Hanna, G. Chem. Phys. Lett. 2013, 573, 77-83), we demonstrated the ability of two approximate solutions of the quantum-classical Liouville equation (QCLE) for qualitatively capturing the electronic dynamics in the pump-probe transient absorption (TA) signal of a model of a condensed phase photoinduced electron transfer reaction whose ground and excited donor states have the same equilibrium geometry. However, the question remained as to the ability of these solutions to treat the more complex situation in which the electronic states are coupled to a low-frequency inner-sphere harmonic vibrational mode (representing an intramolecular mode of the donor-acceptor complex) that shifts their equilibrium geometries with respect to each other and thereby gives rise to signatures of vibrational dynamics in the TA signal. Thus, in this study, we investigated this situation by treating the vibrational mode both quantum mechanically and classically within the context of the approximate Poisson bracket mapping equation (PBME) and forward-backward trajectory solutions (FBTS) of the QCLE. Depending on the definition of the quantum subsystem, both PBME and FBTS are capable of qualitatively capturing several of the main features in the exact TA signal and quantitatively capturing the characteristic time scale of the vibrational dynamics, despite the moderately strong subsystem-bath coupling in this model. Particularly, we found that treating the vibrational mode quantum mechanically using either PBME or FBTS better captures the signatures of the vibrational dynamics, while treating it classically using FBTS better captures the decay in the signal. These findings underscore the utility of the PBME and FBTS approaches for efficiently modeling and interpreting TA signals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".