Multi-principal element materials: Structure, property, and processing
Bibliographic record
Abstract
Materials with multiple principal elements and under different names, such as high-entropy alloys (HEAs) and complex concentrated alloys (CCAs), 1 are attracting much attention due to their excellent structural, mechanical, and functional properties that can lead to a plethora of applications.This special issue covers a wide array of emerging topics, encompassing the fabrication, processing, structure, and properties of multi-principal element alloys (MPEAs).Starting from processing, Mooraj et al. 2 tackle the challenge of printing defects in additively manufactured metal alloys.Their research provides fundamental insights into the origins of printing defects and their profound impact on the mechanical properties of additively manufactured CoCrFeNi HEA.By understanding and mitigating printing defects, the quality and reliability of additively manufactured metal components can be significantly improved.Much of the seminal HEA work relied on fabrication techniquessuch as levitation furnaces-that allowed for very clean experiments to be performed, but that could not realistically be employed in industrial applications.The study by Mooraj et al. emphasizes challenges that arise when more industrially viable methods are employed.In particular, additive manufacturing will likely provide the bridge between the laboratory and applications, as it is very well suited for prototyping.This article gives some guidance to control interlayer porosity that will likely prove useful for future work in this high-momentum field.Moving on to structures, the majority of the articles explore the unique defect structure and energetics at various length scales.Specifically, Shi et al. 3 investigate the spatial inhomogeneity of point defect properties in refractory MPEAs with short-range order.Their work provides insights into tuning the radiation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".