Generalized input-output method to quantum transport junctions. II. Applications
Bibliographic record
Abstract
We apply our generalized input-output method (GIOM), introduced in the previous paper of this series, to prototype molecular junction models describing vibrationally coupled electron transport. In short junctions where analytic treatments are available, we show that the charge current obtained by the GIOM reduces to known limits and reasonably agrees with exact numerical simulations (when available). For extended junctions, we numerically reveal that the current displays a turnover from phonon-assisted to phonon-suppressed transport as a function of electronic and vibrational parameters. As an additional application, we consider a cavity-coupled molecular junction. Here we identify a cavity-induced suppression of charge current in the single-site case, and observe the coexistence of phononic and photonic sidebands in the current-voltage characteristics when both strong light-matter interaction and electron-vibration coupling are present. Together with the first paper of this series, we demonstrate that the input-output framework, which is normally employed in quantum optics, can serve as a powerful and feasible tool in the realm of electron transport junctions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".