Vibronic Structure of the UV/Visible Absorption Spectra of Phenol and Phenolate: A Hybrid Density Functional Theory─Doktorov’s Quantum Algorithm Approach
Bibliographic record
Abstract
The Doktorov’s quantum algorithm has been enacted in combination with time-dependent density functional theory (TD-DFT) to simulate the vibronic structure of the UV/visible absorption spectra of the phenol and phenolate molecules. On the one hand, DFT and TD-DFT are employed with classical algorithms to calculate the ground and excited-state electronic structures as well as their vibrational frequencies and normal modes, whereas, on the other hand, quantum algorithms are employed for evaluating the vibrational transition intensities. In comparison to a previous study, J. Phys. Chem. A 2024, 128, 4369–4377, which demonstrated Doktorov’s quantum algorithm as a proof of concept to predict the vibronic structure of ionization spectra, it is applied here to medium-size molecules with more than 30 vibrational normal modes, without accounting for Duschinsky rotations due to software limitations. This application to simulate the vibronic structures of the spectra of phenol and phenolate also enables assessing the impact of the differences in vibrational frequencies between the ground and excited electronic states.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".