The most sustainable high entropy alloys for the future
Bibliographic record
Abstract
Abstract Because of the enormous number of potential compositions comparable to the number of stars in the universe, high entropy alloys (HEAs) are a virtually endless source of materials possessing versatile properties. Among them, HEAs are promising substitutes for critical elements such as rare earths or platinum group metals. Random or incremental development methods are neither practical nor efficient for exploration. Targeted guessing with sustainability in mind is a necessary enabler, but choosing the suitable sustainable composition of HEAs is challenging. In this paper, we examine the most sustainable HEAs among 30,201 alloys and extract the best 500 that will most likely shape the future of humanity. We consider various sustainability criteria such as carbon footprint, ESG ratings, production compatibility levels and reserves. The results provide a roadmap for HEA scientists and direct their experimental efforts towards the most sustainable compositions, supporting industry efforts while preserving time and resources.
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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 | 0.001 | 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 | 0.001 |
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 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".