An <i>ab initio</i> method for the prediction of the lattice thermal transport properties of oxide systems: Case study of Li2O and K2O
Bibliographic record
Abstract
A method for the prediction of the thermal transport properties of macroscopic and isotropic oxides systems, above the standard temperature of T=298.15 K, is presented. This method combines: (i) the kinetic theory, (ii) a thermodynamically self consistent method for the density of the lattice vibration energy, and (iii) the three-phonon umklapp processes for the description of the phonon-phonon scattering. The proposed approach is purely predictive, as no experimental data are required for the model parameterization; they are derived from ground sate electronic structure calculations. Case studies on Li2O and K2O are presented and discussed. The predicted thermal transport properties are found to be in excellent agreement with available experimental data. The thermal conductivity of K2O is found to differ from the thermal conductivity of Li2O by an order of magnitude. This difference is explained in terms of electron localization within the crystal.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".