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 mod...
