Effects of quantum corrections and isotope scattering on silicon thermal properties
Bibliographic record
Abstract
A quantum correction procedure is proposed to correct silicon thermal properties estimated with molecular dynamics (MD). The procedure considers the energy quantization per mode basis and the anharmonic nature of the potential energy function (including the thermal expansion of the crystal) and is applied to reported thermal properties of silicon estimated with MD in ref. [11], such as temperature, specific heat and thermal conductivity. The procedure facilitates the use of these properties as input to faster numerical methods, such as those based on the Boltzmann transport equation under the single relaxation time approximation. In addition, the effect of isotope scattering is included in reported values of phonon-phonon relaxation times. The effects of the correction procedure and the scattering with isotopes are analyzed in terms of the change of phonon specific heat, mean free path and thermal conductivity. We have found that the application of quantum corrections yields a significant reduction in the contribution of high-frequency modes to the overall thermal conductivity. This contribution is further reduced by the inclusion of isotope scattering. At 220 K, the total contribution of optical modes reduces from 12.3 % (before quantum corrections) to 5.8 %; and to 2 % when the isotope scattering is also considered. The quantum corrections and the inclusion of isotope scattering are found to bring the estimated thermal conductivity into close agreement with experimental values. The relative contributions of the acoustic and optical modes after quantum corrections agrees very well with recently reported ab initio results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".