Open access
Jul 2026
Accelerating Bulk Modulus Design of High-Entropy Alloys Through Explainable Machine Learning and SHAP-Driven Insights
This work presents an interpretable machine learning (ML) system that uses composition- and physics-based descriptors to predict the bulk moduli of high-entropy alloys (HEAs) by permitting precise and computationally efficient bulk modulus prediction, as well as physically significant insights into descriptor–property connections.
Sandeep Jain, Naresh Kumar Wagri, Sunil Dohare et al.
· Metals · 0 citations