Interpretable machine learning excavates a low-alloyed magnesium alloy with strength-ductility synergy based on data augmentation and reconstruction
Bibliographic record
Abstract
• This work proposed an interpretable ML method based on data augmentation and reconstruction. • The model's prediction accuracy exceeded 95 % (R 2 ) for UTS and EL. • A new as-extruded MZAX2000 alloy with a strength-ductility synergy was developed. • Heterogeneous fibrous structure acted a crucial role in enhancing the strength and ductility of low-alloyed mg alloys. The application of machine learning in alloy design is increasingly widespread, yet traditional models still face challenges when dealing with limited datasets and complex nonlinear relationships. This work proposes an interpretable machine learning method based on data augmentation and reconstruction, excavating high-performance low-alloyed magnesium (Mg) alloys. The data augmentation technique expands the original dataset through Gaussian noise. The data reconstruction method reorganizes and transforms the original data to extract more representative features, significantly improving the model's generalization ability and prediction accuracy, with a coefficient of determination (R 2 ) of 95.9 % for the ultimate tensile strength (UTS) model and a R 2 of 95.3 % for the elongation-to-failure (EL) model. The correlation coefficient assisted screening (CCAS) method is proposed to filter low-alloyed target alloys. A new Mg-2.2Mn-0.4Zn-0.2Al-0.2Ca (MZAX2000, wt%) alloy is designed and extruded into bar at given processing parameters, achieving room-temperature strength-ductility synergy showing an excellent UTS of 395 MPa and a high EL of 17.9 %. This is closely related to its hetero-structured characteristic in the as-extruded MZAX2000 alloy consisting of coarse grains (16 %), fine grains (75 %), and fiber regions (9 %). Therefore, this work offers new insights into optimizing alloy compositions and processing parameters for attaining new high strong and ductile low-alloyed Mg alloys.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".