Influence of string-like cooperative atomic motion on surface diffusion in the (110) interfacial region of crystalline Ni
Bibliographic record
Abstract
Although we often think about crystalline materials in terms of highly organized arrays of atoms, molecules, or even colloidal particles, many of the important properties of this diverse class of materials relating to their catalytic behavior, thermodynamic stability, and mechanical properties derive from the dynamics and thermodynamics of their interfacial regions, which we find they have a dynamics more like glass-forming (GF) liquids than crystals at elevated temperatures. This is a general problem arising in any attempt to model the properties of naturally occurring crystalline materials since many aspects of the dynamics of glass-forming liquids remain mysterious. We examine the nature of this phenomenon in the "simple" case of the (110) interface of crystalline Ni, based on a standard embedded-atom model potential, and we then quantify the collective dynamics in this interfacial region using newly developed methods for characterizing the cooperative dynamics of glass-forming liquids. As in our former studies of the interfacial dynamics of grain-boundaries and the interfacial dynamics of crystalline Ni nanoparticles (NPs), we find that the interface of bulk crystalline Ni exhibits all the characteristics of glass-forming materials, even at temperatures well below the equilibrium crystal melting temperature, Tm. This perspective offers a new approach to modeling and engineering the properties of crystalline materials.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".