Understanding the bond-energy, hardness, and adhesive force from the phase diagram via the electron work function
Bibliographic record
Abstract
Properties of metallic materials are intrinsically determined by their electron behavior. However, relevant theoretical treatment involving quantum mechanics is complicated and difficult to be applied in materials design. Electron work function (EWF) has been demonstrated to be a simple but fundamental parameter which well correlates properties of materials with their electron behavior and could thus be used to predict material properties from the aspect of electron activities in a relatively easy manner. In this article, we propose a method to extract the electron work functions of binary solid solutions or alloys from their phase diagrams and use this simple approach to predict their mechanical strength and surface properties, such as adhesion. Two alloys, Fe-Ni and Cu-Zn, are used as samples for the study. EWFs extracted from phase diagrams show same trends as experimentally observed ones, based on which hardness and surface adhesive force of the alloys are predicted. This new methodology provides an alternative approach to predict material properties based on the work function, which is extractable from the phase diagram. This work may also help maximize the power of phase diagram for materials design and development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".