Bibliographic record
Abstract
A state-of-the-art thermodynamic database has been developed for multicomponent oxide systems. It can be used in combination with FactSage software to calculate the properties of metallurgical slags, glasses, ceramics, refractories, minerals, cements, etc. The database has been developed by collecting all available structural, thermodynamic, and phase equilibria data for a particular chemical system, critical evaluation of this information, developing a thermodynamic model for each solution phase and optimization of model parameters to reproduce the experimental data. Then the models are used to estimate the thermodynamic properties of multicomponent solutions from the properties of lower-order subsystems. Oxide phases often exhibit complex structures and strong interactions between components, which require more sophisticated models than are normally used, for example, for metal alloys. Short-range ordering is rather common and random mixing is often not a good approximation. The models for multicomponent liquid and solid solutions have been developed within the Modified Quasichemical Formalism and Compound Energy Formalism. Optimized model equations are consistent with thermodynamic principles and fully characterize a chemical system, requiring much less experimental work to achieve this goal since only a few measurements are needed in higher-order systems to validate the model estimates. The database can be readily used in conjunction with the FactSage Gibbs energy minimization software to calculate any stable or metastable phase equilibria and phase diagrams. The present article outlines the major components and phases that are currently available in the oxide database, as well as the most important features of the models that have been developed. The model and database have also been developed for the viscosity of oxide melts and glasses. The model links the viscosity to the structure of the liquid phase, which is estimated using the thermodynamic database.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".