<i>Ab initio</i> characterization of coupling strength for all types of dangling-bond pairs on the hydrogen-terminated Si(100)-2 × 1 surface
Bibliographic record
Abstract
Recent work has suggested that coupled silicon dangling bonds sharing an excess electron may serve as building blocks for quantum-cellular-automata cells and quantum computing schemes when constructed on hydrogen-terminated silicon surfaces. In this work, we employ ab initio density-functional theory to examine the details associated with the coupling between two dangling bonds sharing one excess electron and arranged in various configurations on models of phosphorous-doped hydrogen-terminated silicon (100) surfaces. Our results show that the coupling strength depends strongly on the relative orientation of the dangling bonds on the surface and on the separation between them. The orientation of dangling bonds is determined by the anisotropy of the silicon (100) surface, so this feature of the surface is a significant contributing factor to variations in the strength of coupling between dangling bonds. The results demonstrate that simple models for approximating tunneling, such as the Wentzel-Kramer-Brillouin method, which do not incorporate the details of surface structure, are incapable of providing reasonable estimates of tunneling rates between dangling bonds. The results provide guidance to efforts related to the development of dangling-bond based computing elements.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".