Exploring the Compositional Space of High-Entropy Alloys for Cost-Effective High-Temperature Applications
Why this work is in the frame
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Bibliographic record
Abstract
High-entropy alloys (HEAs) are nearly equimolar multi-principal element alloys, exhibiting exceptional thermal and mechanical properties at extreme conditions such as high-temperatures and stresses. Since the first discovery and early conceptualization of conventional HEAs nearly two decades ago, HEAs with far-from-equimolar compositions have attracted substantial interest to provide a broader range of material properties and to adjust price fluctuations and availability of commodities. Here, we present a first-principles investigation of non-equimolar chromium-manganese-iron-cobalt-nickel (CrMnFeCoNi) HEAs and effects of molybdenum (Mo) and niobium (Nb) substitutions on cost, phase stability and solubility, and mechanical and thermal performance up to 1000 K operational temperature. Virtual-crystal approximation is used to expediently approximate random solid solutions at the disordered mean-field limit. Using multi-objective metaheuristics built on a first-principles database, golden compositions are predicted for thermally well-insulated components and effective heat sinks. Replacing Co with Fe lowers commodity costs without hindering phase stability and solubility. Lower Ni concentration leads to lower thermal conductivity, indicating better thermal insulation, while reducing Mn concentration significantly increases the thermal conductivity, indicating better performing heat sinks. Moving away from equimolar ratios commonly increases the thermal expansion coefficient, which could generate higher thermal stresses. Nb and Mo substitution always lead to substantially higher commodity cost and density but with an increment in the mechanical performance due to solid-solution hardening. However, alloying with Mo and Nb is the only compositional space that reduces the thermal conductivity and thermal expansion coefficient.
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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 | 0.000 | 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 | 0.000 |
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