Thermodynamic modeling with uncertainty quantification using the modified quasichemical model in quadruplet approximation: Implementation into PyCalphad and ESPEI
Bibliographic record
Abstract
The modified quasichemical model in the quadruplet approximation (MQMQA) considers the first- and the second-nearest-neighbor coordination and interactions, becoming one of the best models to describe short-range ordering in complex liquids such as molten salts, slag in metal processing, and electrolytic solutions. The present work implements the MQMQA into Python based open-source software PyCalphad for thermodynamic calculations using the CALPHAD approach. This endeavor facilitates the development of MQMQA-based thermodynamic database with uncertainty quantification (UQ) using the open-source software ESPEI. New database format XML (extensible markup language) has been proposed to extensively parse other standard database formats. Taking the KF-NiF2 system as an example, we demonstrate the successful implementation of MQMQA through thermodynamic calculations of, e.g., Gibbs energy, equilibrium quadruplet fractions, and phase diagram, using PyCalphad, as well as database development with UQ using ESPEI. The present implementation indicates that the open-source PyCalphad/ESPEI could provide an accurate alternative to perform CALPHAD modeling for complex liquids with short-range ordering, making the MQMQA more accessible to the broad community.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".