Role of string-like collective atomic motion on diffusion and structural relaxation in glass forming Cu-Zr alloys
Bibliographic record
Abstract
We investigate Cu-Zr liquid alloys using molecular dynamics simulation and well-accepted embedded atom method potentials over a wide range of chemical composition and temperature as model metallic glass-forming (GF) liquids. As with other types of GF materials, the dynamics of these complex liquids are characterized by "dynamic heterogeneity" in the form of transient polymeric clusters of highly mobile atoms that are composed in turn of atomic clusters exhibiting string-like cooperative motion. In accordance with the string model of relaxation, an extension of the Adam-Gibbs (AG) model, changes in the activation free energy ΔGa with temperature of both the Cu and Zr diffusion coefficients D, and the alpha structural relaxation time τα can be described to a good approximation by changes in the average string length, L. In particular, we confirm that the strings are a concrete realization of the abstract "cooperatively rearranging regions" of AG. We also find coexisting clusters of relatively "immobile" atoms that exhibit predominantly icosahedral local packing rather than the low symmetry packing of "mobile" atoms. These two distinct types of dynamic heterogeneity are then associated with different fluid structural states. Glass-forming liquids are thus analogous to polycrystalline materials where the icosahedrally packed regions correspond to crystal grains, and the strings reside in the relatively disordered grain boundary-like regions exterior to these locally well-ordered regions. A dynamic equilibrium between localized ("immobile") and wandering ("mobile") particles exists in the liquid so that the dynamic heterogeneity can be considered to be type of self-assembly process. We also characterize changes in the local atomic free volume in the course of string-like atomic motion to better understand the initiation and propagation of these fluid excitations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".