Thermodynamic database development—modeling and phase diagram calculations in oxide systems
Bibliographic record
Abstract
The databases of the FactSage thermodynamic computer system have been under development for 30 years. These databases contain critically evaluated and optimized data for thousands of compounds and hundreds of multicomponent solutions of solid and liquid metals, oxides, salts, sulfides, etc. The databases are automatically accessed by user-friendly software that calculates complex multiphase equilibria in large multicomponent systems for a wide variety of possible input/output constraints. The databases for solutions have been developed by critical evaluation/optimization of all available phase equilibrium and thermodynamic data. The databases contain parameters of models specifically developed for different types of solutions involving sublattices, ordering, etc. Through the optimization process, model parameters are found which reproduce all thermodynamic and phase equilibrium data within experimental error limits and permit extrapolation into regions of temperature and composition where data are unavailable. The present article focuses on the databases for solid and liquid oxide phases involving 25 elements. A short review of the available databases is presented along with the models used for the molten slag and the solid solutions such as spinel, pyroxene, olivine, monoxide, corundum, etc. The critical evaluation/optimization procedure is outlined using examples from the Al2O3-SiO2-CaO-FeO-Fe2O3 system. Sample calculations are presented in which the oxide databases are used in conjunction with the FactSage databases for metallic and other phases. In particular, the use of the FactSage module for the calculation of multicomponent phase diagrams is illustrated.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".