Application of polyhedron model to predict heat capacity of mixed oxides
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Bibliographic record
Abstract
The heat capacity of mixed oxides can be estimated using a linear summation of the heat capacities of their structural constituent polyhedra. This approach is particularly useful for hygroscopic and volatile oxides, where experimental data can be difficult to obtain. The present work aims to enhance the polyhedron model (PM) by incorporating contributions from second-order transitions, including magnetic and site order-disorders, into C p and expanding it to include ZnO and PbO-containing systems in comparison to the previous version of the model. A regression analysis was performed over the new dataset consisting of the properties of 85 compounds in the system Li-Na-K-Ca-Mg-Mn-Fe-Pb-Zn-Al-Ti-Si-O to obtain optimized C p for 20 constituent polyhedra. We validate the updated PM against experimental data, demonstrating an overall improvement between 7 and 9 % in the estimation of C p compared to the previous version of the model. We also compare the updated model with well-established models in the literature, such as the Neumann-Kopp Rule, and ab-initio calculations. The PM shows higher precision than NKR and the linear summation nature of PM endows the model with simplicity which contrasts with ab-initio calculations. Additionally, the model has demonstrated an inherent self-correction capability relative to the original input values, as shown for K 2 Si 4 O 9 . The model is also applied to predict the heat capacity of 10 compounds in the Na 2 O-PbO-SiO 2 and Na 2 O-ZnO-SiO 2 systems, where experimental data are lacking.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it