<i>Ab initio</i>modeling of quantum transport properties of molecular electronic devices
Bibliographic record
Abstract
We report on a self-consistent ab initio technique for modeling quantum transport properties of atomic and molecular scale nanoelectronic devices under external bias potentials. The technique is based on density functional theory using norm conserving nonlocal pseudopotentials to define the atomic core and nonequilibrium Green's functions (NEGF's) to calculate the charge distribution. The modeling of an open device system is reduced to a calculation defined on a finite region of space using a screening approximation. The interaction between the device scattering region and the electrodes is accounted for by self-energies within the NEGF formalism. Our technique overcomes several difficulties of doing first principles modeling of open molecular quantum coherent conductors. We apply this technique to investigate single wall carbon nanotubes in contact with an Al metallic electrode. We have studied the current-voltage characteristics of the nanotube-metal interface from first principles. Our results suggest that there are two transmission eigenvectors contributing to the ballistic conductance of the interface, with a total conductance $G\ensuremath{\approx}{G}_{0}$ where ${G}_{0}{=2e}^{2}/h$ is the conductance quanta. This is about half of the expected value for infinite perfect metallic nanotubes.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".