Understanding the Effect of Mo Concentration on the Strength of AlCoCrFeMo High-Entropy Alloy Using Atomistic Simulations
Bibliographic record
Abstract
Abstract High-entropy alloys (HEAs) represent a new class of advanced metallic alloys that have gained significant interest. They offer a unique combination of mechanical, thermal, and functional properties, making HEAs ideal for various industrial applications. One such alloy is the recently developed equiatomic body-centred cubic phase AlCoCrFeMo. In particular, thermally sprayed AlCoCrFeMo coatings have gained wide interest due to their exceptional mechanical properties compared to common industrial steel. In the current study, the effect of Mo concentrations on the strength of single crystal AlCoCrFeMo HEA was investigated using molecular dynamics simulation and the phase stability of the alloy was studied using polyhedral template matching. Our results indicate that the local lattice distortion of the alloy is not significantly related to Mo concentration. The yield strength of AlCoCrFeMo HEA obtained through tensile loading, was found to increase with Mo concentration, at a molar ratio of Mo higher than 0.5. Investigation of the deformation behavior of the HEA revealed that bands with high shear strains evolved during plastic deformation. The formation of shear bands after the yield point elucidated the softening exhibited by the material due to localized deformation. These findings provide guidance for tailoring the mechanical properties of AlCoCrFeMo HEA by adjusting Mo concentrations, offering new avenues for designing functional coating 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".