CatBoost and other ensemble ML models achieve R² scores of 0.95, 0.916, and 0.903 on yield strength, ultimate tensile strength, and elongation for resorbable Mg alloys, with SHAP analysis highlighting processing conditions and Zn/Mn/Gd content as key drivers.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.mtrl-sci 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Accelerating the Design of Resorbable Magnesium Alloys: A Machine Learning Approach to Property Prediction
CatBoost and other ensemble ML models achieve R² scores of 0.95, 0.916, and 0.903 on yield strength, ultimate tensile strength, and elongation for resorbable Mg alloys, with SHAP analysis highlighting processing conditions and Zn/Mn/Gd content as key drivers.