Accelerated discovery of nanostructured high-entropy alloys and multicomponent alloys via high-throughput strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it