The Prediction of the Thermal Conductivity of Gallium Arsenide: A Molecular Dynamics Study
Bibliographic record
Abstract
Gallium arsenide is the second most used semiconductor material with applications in light-emitting diodes, field-effect transistors, and integrated circuits. Thus, understanding and controlling the thermal conductivity of gallium arsenide is crucial to design devices for such applications. The goal of this study is to predict the thermal conductivity of gallium arsenide as a function of temperature and vacancy concentration. Thermal conductivities are predicted using an equilibrium molecular dynamics method based on the Green-Kubo formalism with temperatures between 300 K and 900 K and vacancy concentrations up to 0.5%. Our results show that the thermal conductivities of the vacancy-free system predicted by our model are in good agreement with experimental values around the Debye temperature. In addition, our model predicts that conductivities significantly decrease with increasing vacancy concentration. At 300 K conductivities drop by 39.5% with a 0.1% defect content and 74.4% with 0.5% respect to that of the pure system. The power spectra of thermal conductivities and heat current autocorrelation functions indicate that phonon scattering produced near the vacancies reduces the contribution of the acoustic frequencies. The density of states quantifies the decrease of acoustic and optic frequencies by increasing the vacancy concentration.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".