Phase formation prediction in magnetron sputtered Cu(Ti)Zn thin films: Numerical vs experimental approaches
Bibliographic record
Abstract
In this work, we evaluated the ability of three numerical methods to predict the phase formation in Cu–Zn binary and Cu–Ti–Zn ternary alloy thin films deposited by DC-magnetron sputter deposition. Molecular dynamics (MD) simulations were carried out to simulate the growth of the alloy film and study the organization at the atomic level. A Machine Learning (ML) approach trained with a recently published bulk HEA (high-entropy alloy) database was used to determine the presence of an amorphous phase, solid solutions, or/and intermetallics. Finally, CALPHAD (CALculation of PHAse Diagrams) thermodynamic modeling allows one to simulate the phase diagrams. Crystalline phases formed in experimental films were investigated by grazing incidence x-ray diffraction (GIXRD). Comparison with CALPHAD results highlights that for pure Ti or binary Cu–Zn films, the thermodynamically stable phases are formed in the films. Less agreement was found at low or high percentage of Ti introduced in the Cu–Zn system, and drastic differences were observed for elemental compositions close to equimolarity. In those cases, the out of equilibrium nature of the magnetron sputtering deposition technique is evidenced. The very limited agreement between the GIXRD and ML approach is explained by the available database, which is exclusively based on bulk alloys. Elemental composition of the alloy does not itself determine the stabilized phases: elaboration techniques are to be taken into account too. MD simulations bring information on a possible segregation of the Zn element to the surface and grain boundaries. A very good agreement is evidenced between the calculated and experimental diffraction patterns.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".