Theory of atomic scale quantum dots in silicon: dangling bond quantum\n dots on silicon surface
Bibliographic record
Abstract
We present here a theory and a computational tool, Silicon-{\\sc Qnano}, to\ndescribe atomic scale quantum dots in Silicon. The methodology is applied to\nmodel dangling bond quantum dots (DBQDs) created on a passivated\nH:Si-(100)-(2$\\times$1) surface by removal of a Hydrogen atom. The electronic\nproperties of DBQD are computed by embedding it in a computational box of\nSilicon atoms. The surfaces of the computational box were constructed by using\nDFT as implemented in {\\sc Abinit} program. The top layer was reconstructed by\nthe formation of Si dimers passivated with H atoms while the bottom layer\nremained unreconstructed and fully saturated with H atoms. The computational\nbox Hamiltonian was approximated by a tight-binding (TB) Hamiltonian by\nexpanding the electron wave functions as a Linear Combination of Atomic\nOrbitals and fitting the bandstructure to {\\it ab-initio} results. The\nparametrized TB Hamiltonian was used to model large finite Si(100) boxes\n(slabs) with number of atoms exceeding present capabilities of {\\it ab-initio}\ncalculations. The removal of one hydrogen atom from the reconstructed surface\nresulted in a DBQD state with wave function strongly localized around the Si\natom and energy in the silicon bandgap. The DBQD could be charged with zero,\none and two electrons. The Coulomb matrix elements were calculated and the\ncharging energy of a two electron complex in a DBQD obtained.\n
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".