Accelerated discovery of nanostructured high-entropy alloys and multicomponent alloys via high-throughput strategies
Bibliographic record
Abstract
Nanostructured materials (NsMs) exhibit many interesting and useful properties; yet their grain sizes or phases are generally unstable at elevated temperatures, limiting their process methods and engineering applications. Many emerging alloys, especially high-entropy alloys (HEAs) and related multicomponent alloys, are reported to show enhanced thermal stability and mechanical strength. The identification of mechanically strong and thermally stable multicomponent alloys out of a vast compositional space, however, is a daunting task – many are predominantly developed through sequential and time-consuming trial-and-error approaches. Thus, high-throughput strategies are urgently needed to accelerate the discovery of new and useful nanostructured HEAs (Ns-HEAs). As the fields of Ns-HEAs and high-throughput methods are developing rapidly, an avenue of research on this topic is to be exploited. This review focuses on the literature on the high-throughput fabrication, characterization, and testing of the microstructures, phases, compositions, mechanical properties, and thermal stabilities of a wide range of Ns-HEAs reported over the past two decades. This article also includes recent high-throughput methods that could be potentially used for the discovery of new Ns-HEAs and related multicomponent alloys, as well as various high-throughput data analysis methods such as theoretical screening, simulation, and machine learning. The article concludes with progress, challenges, and opportunities about the future directions in the accelerated discovery of a wide range of complex alloys via high-throughput methodologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".