Phase Prediction Methodologies for Rapid Screening of High Entropy Alloys
Bibliographic record
Abstract
Abstract Increasing gas turbine hot section temperatures is a venue to improve fuel efficiency, thus pushing the need for new, higher temperature resistant materials. Experimental results show that high entropy alloys (HEAs), consisting of near equiatomic amounts of various metallic elements with no clear solvent, exhibit excellent high temperature mechanical properties, corrosion resistance, and good strength to weight ratios. This has resulted in significantly increased interest in studying HEAs for potential gas turbine application over the past decade. The properties of HEAs tend to be highly sensitive to phase composition; however, pre-existing phase prediction methodologies used for traditional alloy compositions, with a clearly defined solvent, are generally not well suited to HEAs. Additionally, the combination of the vast design space and complex elemental interactions render experimental exploration of new HEAs unfeasible at scale, necessitating higher throughput methods. Computer aided calculation of phase diagrams and first principal calculations are well established methods for predicting the phase structure of hypothetical alloy compositions, however they tend to be very computationally intensive and thus their speed is highly limited by available hardware. Two prediction methods capable of rapidly screening candidate composition are empirically developed design parameters based on values derived from the elemental composition of the alloys and machine learning models trained using available experimental HEA phase compositions. This simplifies the calculations and the input parameters required for both methodologies are readily available. This work compares the effectiveness of a variety of empirical design parameters and a pre-trained machine learning model, based on a convolutional neural network architecture, at predicting the resultant phases in various HEA compositions, including the W-Nb-Mo-Ta-Ti-Zr alloy system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".