Modified polyhedron model for predicting standard enthalpy of formation and entropy of mixed oxides
Bibliographic record
Abstract
Thermodynamic modeling of oxidic systems is crucial in advancing various fields of science and technology . Polyhedron Model (PM) estimates the standard enthalpy of formation and entropy of mixed oxides via the linear summation of the thermodynamic properties of constituent polyhedra. Each polyhedron consists of a centered cation with neighboring oxygen anions; hence, the model accounts for the interaction between anions and cations. While second-order transitions have been considered in previous iterations of the model, the PM has certain shortcomings, including neglect of variations in polyhedron volume, polyhedron distortion, inter-polyhedron linkage, and second nearest-neighbor or higher-order interactions, which are not negligible. The present work introduces the Modified Polyhedron Model (MPM), which aims to incorporate these contributions through a neural network (NN) model to improve the accuracy of predictions for standard enthalpy of formation ( Δ H 298 K o ) and standard entropy ( S 298 K o ). This is possible by using the residuals from the PM as inputs to the NN model, whose outputs are the calculated thermodynamic properties of compounds. The dataset consists of 155 compounds in the Li-Na-K-Ca-Mg-Mn-Fe-Al-Ti-Si-O system, classified by 20 polyhedra. The MPM considerably reduces the error in predicting enthalpy of formation and entropy, improving the alignment with experimental values across most analyzed compounds in comparison with the PM. These results suggest that the MPM can significantly improve the predictability of thermodynamic properties for mixed oxides .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".