RHF and DFT Study of the Molecular and Electronic Properties of (SiO2)n and (GeO2)n Nanoclusters
Bibliographic record
Abstract
The study of nanoclusters has attracted a lot of scientific research over the years. This class of materials are important because they bridge the gap between bulk materials and molecular structures. Silicon and Germanium oxides have many applications in semiconductor technology and nanotechnology. In this research work, molecular and electronic properties of Silicon and Germanium dioxide nanoclusters are studied. The results obtained reveal the comparative advantages and disadvantages of using any of the two oxides for particular applications. Restricted Hartree Fock and Density Functional Theory computations of the molecular and electronic properties of (SiO2)n and (GeO2)n nanoclusters (n = 1,..,6) are studied. Silicon dioxide clusters are found to have higher thermal energies and lower average bond lengths and are thus more stable than Germanium dioxide clusters. At n = 1, both nanoclusters are non-polar, but gradually become more polar with increase in n. The average polarizability, molecular hyperpolarizability and total thermal energies of the nanoclusters increases with increase in molecular size. Computed values of the electron affinities for (SiO2)n clusters agree with experimental results. Some of the most intense Infra Red vibrational motions observed in both molecules are anti-symmetric stretching of Si=O/Ge-O and chain in plane, symmetric stretching of the Si=O/Ge-O bonds and chain in plane and symmetric twisting/breathing of the chain(s) in plane. The two nanoclusters are also Raman active at some frequencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".