Formulation of Temperature-Dependent Thermal Conductivity of NaF, β-Na<sub>3</sub>AlF<sub>6</sub>, Na<sub>5</sub>Al<sub>3</sub>F<sub>14</sub>, and Molten Na<sub>3</sub>AlF<sub>6</sub> Supported by Equilibrium Molecular Dynamics and Density Functional Theory
Bibliographic record
Abstract
Numerical modeling of heat transport in the side ledge of a cryolite bath as a function of its thickness requires an accurate knowledge of the thermal transport properties of the cryolite bath as well as the solid phases constituting the side ledge. Unfortunately, due to experimental limitations, there is a lack of experimental data on this subject. The aim of this work is to predict these otherwise unknown properties. The thermal conductivity of major constituents of the side ledge in their solid state, namely: NaF, β– Na 3 AlF 6 and Na 5 Al 3 F 14 as well as molten Na 3 AlF 6 are formulated as a function of temperature. To achieve this, we have performed a series of equilibrium molecular dynamics simulations (EMD) in both the NPT (isobar-isotherm) and NVT (canonical) statistical ensembles. The proposed approach is purely predictive as the ionic interaction potentials were parametrized on the basis of Density Functional Theory (DFT). The results are then compared to a theoretical model which has been shown to be a very good predictive ability for many ionic and ionocovalent compounds. For solid NaF and liquid Na 3 AlF 6, for which data are available, the predictions were found to be in excellent agreement with experiment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".