Relationships between composition and properties of (Cr/Ti)SiN and (Cr/Ti)CN alloys: an<i>ab initio</i>study
Bibliographic record
Abstract
It has previously been noted that different fcc metal nitrides exhibit different superior properties, including the high hardness of TiN and the excellent corrosion and oxidation resistance of CrN. Si and C have been added into such metal nitrides in order to tailor their functional properties. Contrary to the intensively studied TiSiN and TiCN nanocomposite and solid solution systems, much less is known about the complex relationships between the composition and the properties of CrSiN and CrCN. In fact, there is a qualitative difference between cubic spin-unpolarized materials such as TiN, and spin-polarized materials such as CrN which may exhibit a cubic/orthorhombic structural transformation. In the present work, we report ab initio calculations of the properties of (Cr/Ti)SiN and (Cr/Ti)CN systems of various compositions. We specifically predict the lattice constant, bulk modulus, elastic tensor, shear modulus, Young's modulus, Poisson's ratio, magnetization, electronic structure and preference towards the cubic/orthorhombic structural transformation. Knowledge of the modeled relationships allows one to tailor the material characteristics of various ternary metal nitrides for different technological applications.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".