Molecular conductance obtained in terms of orbital densities and response functions
Bibliographic record
Abstract
Using the source-sink potential (SSP) approach recently developed in our group, we study electron transmission through molecular electronic devices (MEDs). Instead of considering the source-sink potentials exactly, we use a perturbative approach to find an expression for the transmission probability T(E) = 1 - absolute value(r(E))(2) that depends on the properties of the bare molecule. As a consequence, our approach is limited to weak molecule-contact coupling. Provided that the orbitals of the isolated molecule are not degenerate, we show that it is the orbital density, on the atoms that connect the molecule to the contacts, that largely determines the transmission through the device. Corrections to this leading-order contribution involve the second- and higher-order molecular response functions. An explicit expression for T(E) is obtained that is correct up to first order in the molecular response function. Illustrating our approach, a qualitative explanation is provided for why orders of magnitude difference in the transmission probability are obtained [M. Mayor et al., Angew. Chem. Int. Ed. 42, 5834 (2003)] upon modification of the contact position in the molecule. An extension of the formalism to interacting systems is outlined as well.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".