Impact of disorder on the exciton dynamics of an open quantum battery
Bibliographic record
Abstract
Quantum batteries (QBs) have garnered attention as candidates for energy storage devices due to their inherent quantum advantages over classical electrochemical batteries. However, owing to the vulnerability of quantum resources to disorder, the ability of a charged QB to effectively store and discharge energy may be adversely affected by mechanical motions, thermal fluctuations, impurities, and defects in the QB. In this connection, we study the effects of (i) Gaussian static disorder, Gaussian white noise, and Gaussian colored noise in the onsite energies and electronic couplings and (ii) periodic oscillations in the electronic couplings on the exciton storage efficiency and exciton discharge rate of an open excitonic QB model-an open quantum system that stores and discharges excitons. To efficiently average over the many possible noise realizations, we employ an accurate mixed quantum-classical dynamics method that treats the QB quantum mechanically and the thermal baths in a classical-like way. The results reveal that the exciton storage efficiency decreases as the disorder strength increases, with static disorder causing the largest reductions followed by colored noise and white noise. In contrast, the exciton discharge rate remains mainly unaffected by the different types of disorder, even under very strong disorders. Moreover, depending on the model parameters, the incorporation of periodic oscillations into the electronic couplings could either enhance, have no significant effect on, or decrease the exciton discharge rate. Overall, our study elucidates the effects of different types of disorder and inter-site vibrations on the exciton dynamics in a charged QB, thereby shedding light on the importance of environmental noise engineering and mitigation in open QBs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".