Thermodynamic modelling of liquids: CALPHAD approaches and contributions from statistical physics
Bibliographic record
Abstract
We describe current approaches to thermodynamic modelling of liquids for the CALPHAD method, the use of available experimental methods and results in this type of modelling, and considerations in the use of atomic‐scale simulation methods to inform a CALPHAD approach. We begin with an overview of the formalism currently used in CALPHAD to describe the temperature dependence of the liquid Gibbs free energy and outline opportunities for improvement by reviewing the current physical understanding of the liquid. Brief descriptions of experimental methods for extracting high‐temperature data on liquids and the preparation of undercooled liquid samples are presented. Properties of a well‐determined substance, B 2 O 3 , including the glass transition, are then discussed in detail to emphasize specific modelling requirements for the liquid. We then examine the two‐state model proposed for CALPHAD in detail and compare results with experiment and theory, where available. We further examine the contributions of atomic‐scale methods to the understanding of liquids and their potential for supplementing available data. We discuss molecular dynamics (MD) and Monte Carlo methods that employ atomic interactions from classical interatomic potentials, as well as contributions from ab initio MD. We conclude with a summary of our findings.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".