An Experimental High-Throughput to High-Fidelity Study Towards Discovering Al–Cr Containing Corrosion-Resistant Compositionally Complex Alloys
Bibliographic record
Abstract
Abstract Compositionally complex alloys hold the promise of simultaneously attaining superior combinations of properties, such as corrosion resistance, light-weighting, and strength. Achieving this goal is a challenge due in part to a large number of possible compositions and structures in the vast alloy design space. High-throughput methods offer a path forward, but a strong connection between the synthesis of an alloy of a given composition and structure with its properties has not been fully realized to date. Here, we present the rapid identification of corrosion-resistant alloys based on combinations of Al and Cr in a base Al–Co–Cr–Fe–Ni alloy. Previously unstudied alloy stoichiometries were identified using a combination of high-throughput experimental screening coupled with key metallurgical and electrochemical corrosion tests, identifying alloys with excellent passivation behavior. The alloy native oxide performance and its self-healing attributes were probed using rapid tests in deaerated 0.1-mol/L H2SO4. Importantly, a correlation was found between the electrochemical impedance modulus of the exposure-modified air-formed film and self-healing rate of the CCAs. Multi-element extended x-ray absorption fine structure analyses connected more ordered type chemical short-range order in the Ni–Al 1st nearest-neighbor shell to poorer corrosion resistance. This report underscores the utility of high-throughput exploration of compositionally complex alloys for the identification and rapid screening of a vast stoichiometric space. Graphical Abstract
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".