Localization model description of diffusion and structural relaxation in glass-forming Cu–Zr alloys
Bibliographic record
Abstract
Abstract We test the localization model (LM) prediction of a parameter-free relationship between the α -structural relaxation time τ α and the Debye–Waller factor 〈 u 2 〉 for a series of simulated glass-forming Cu–Zr metallic liquids having a range of alloy compositions. After validating this relationship between the picosecond (‘fast’) and long-time relaxation dynamics over the full range of temperatures and alloy compositions investigated in our simulations, we show that it is also possible to estimate the self-diffusion coefficients of the individual atomic species ( D Cu , D Zr ) and the average diffusion coefficient D using the LM, in conjunction with the empirical fractional Stokes–Einstein (FSE) relation linking these diffusion coefficients to τ α . We further observe that the fragility and extent of decoupling between D and τ α strongly correlate with 〈 u 2 〉 at the onset temperature of glass-formation T A where particle caging and the breakdown of Arrhenius relaxation first emerge.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".