Investigating the atomic level influencing factors of glass forming ability in NiAl and CuZr metallic glasses
Bibliographic record
Abstract
Bulk metallic glasses are a relatively new class of amorphous metal alloy which possess unique mechanical and magnetic properties. The specific concentrations and combinations of alloy elements needed to prevent crystallization during melt quenching remains poorly understood. A correlation between atomic properties that can explain some of the previously identified glass forming ability (GFA) anomalies of the NiAl and CuZr systems has been identified, with these findings likely extensible to other transition metal-transition metal and transition metal-metalloid (TM-M) alloy classes as a whole. In this work, molecular dynamics simulation methods are utilized to study thermodynamic, kinetic, and structural properties of equiatomic CuZr and NiAl metallic glasses in an attempt to further understand the underlying connections between glass forming ability, nature of atomic level bonding, short and medium range ordering, and the evolution of structure and relaxation properties in the disordered phase. The anomalous breakdown of the fragility parameter as a useful GFA indicator in TM-M alloy systems is addressed through an in-depth investigation of bulk stiffness properties and the evolution of (pseudo)Gruneisen parameters over the quench domain, with the efficacy of other common glass forming ability indicators similarly being analyzed through direct computation in respective CuZr and NiAl systems. Comparison of fractional liquid-crystal density differences in the two systems revealed 2-3 times higher values for the NiAl system, providing further support for its efficacy as a general purpose GFA indicator.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".