Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

ABSTRACT A continuing challenge in aerospace materials is the search for alloys that have desired functional properties to operate at higher temperatures with lower densities. This improves aero‐engine efficiency and reduces CO 2 and other harmful emissions, aligning with aviation industry targets for emission reduction. Nb─Si alloys can operate at higher temperatures than current Ni‐based superalloys and have lower densities. We develop a machine learning‐driven design framework for Nb─Si based ultra‐high temperature alloys using a three‐step feature screening strategy to break the 20 MPa·m 1/2 fracture toughness barrier. Our model predicts fracture toughness ( K Q ) with an error