Predicting the density of molten alloys using computational thermodynamics
Bibliographic record
Abstract
The density of a molten alloy can be calculated from the quotient of its molar mass divided by its molar volume. The molar volume of a molten alloy, however, often deviates from the average of the molar volumes of its constituents. The deviation is caused mainly by the affinity (or lack of it) between dissimilar atoms, which can be quantified by the enthalpy of mixing. Up to now, the link between the enthalpy of mixing and the volume change has been determined empirically through the regression of experimental measurements of alloy densities. In the present study, the derivative of molar volume with respect to enthalpy was deduced and the molar volumes of molten alloys were computed entirely based on the properties of pure elements and the enthalpy of mixing of the alloys. The very slight increase in the packing density due to the size difference of different atoms was also considered. The effect of cluster formation due to short range ordering was also addressed. Over six hundred data points were used in validations. Excellent agreements were achieved between the calculated values and the experimental measurements.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".