Machine learning prediction of flexural strength of high‐entropy carbides via feature‐engineered ensemble modeling

Abstract Motivated by the imperative for radiation‐resistant structural materials in next‐generation nuclear reactors, high‐entropy carbide ceramics (HECs) have gained prominence as candidate materials for extreme high‐temperature and irradiation environments. Confronted with the inherent complexity of HECs that impedes reliable flexural strength prediction, this study pioneers a machine learning framework to decode composition–structure–property relationships. Six rigorously compared algorithms identified VotingRegressor as the optimal predictor, leveraging four key features screened via multicollinearity analysis. The model achieved exceptional accuracy with experimental validation on (Ti,Zr,Hf,Nb,Ta)C specimens confirming prediction robustness. This paradigm demonstrates the transformative role of machine learning in accelerating nuclear‐grade ceramic design, bridging atomic‐scale features to macroscopic mechanical performance.