Design and optimization of quinary high entropy alloy systems with single-phase microstructures from conventional alloy systems
Bibliographic record
Abstract
High-entropy alloy (HEA) discovery has traditionally relied on theoretical stability criteria and random compositional permutations, often overlooking practical manufacturability and experimental viability. In this study, we present a constraint-driven alloy design framework that enables early-stage development of manufacturable quinary equiatomic HEAs derived directly from conventional engineering alloys. Beyond the classical thermodynamic screening parameters, our framework incorporates manufacturability filters: melting point compatibility (ΔT mc ), atomic solubility index, ( S ¯ ) and vapor pressure parameter (P v ), targeting additive manufacturing (AM) processability. A flexible web-based platform was developed, allowing users to input any conventional alloy and generate new HEA systems according to the described criteria. We applied this framework to 15 industrial prototype alloys, generating over 60 quinary HEA candidates, many of which are previously unreported. One predicted composition, CuFeNiMnAl, was successfully fabricated using Directed Energy Deposition (DED), exhibiting a fine-grained FCC structure, high hardness (∼560 HV), and high densification (∼7.1 g/cm 3 ) relative to its parent alloy, nickel aluminum bronze. This experimental validation confirms the framework’s ability to deliver novel, manufacturable HEAs derived from real alloy systems. By integrating manufacturability constraints into early-stage design, this work provides a scalable, data-efficient pathway to application-ready HEAs, bridging computational discovery with industrial implementation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".