Data‐Driven Design of Spinodal Decomposition in (Ti, Zr, Hf)C Composite Carbides for Optimizing the Hardness‐Toughness Trade‐Off

Abstract Transition metal carbides, characterized by exceptional hardness, wear resistance, and thermal stability, emerge as promising candidates for extreme‐environment applications. However, the inherent hardness‐toughness trade‐off remains a critical challenge for development of the high‐performance ceramics. Herein, a data‐driven design strategy to optimize this trade‐off through precisely tailoring spinodal decomposition in (Ti, Zr, Hf)C composite carbides is proposed. The integration of phase diagram calculations, key experiments, and machine learning approaches permits high‐throughput mechanical property screening across broad compositional and temperature ranges. Isothermal aging induces the formation of a high density of nanoscale nodular structures within the (Ti, Zr, Hf)C carbides, accompanied by the generation of dislocations, synergistically enhancing the hardness (2780 H V ) and fracture toughness (3.47 MPa·m 1/2 ) by 32% and 80%, respectively, compared to the as‐sintered state. By establishing a data‐driven framework that elucidates the composition‐processing‐property relationships, this research provides a scientific strategy to rapidly optimize the hardness and toughness in carbide ceramics through orchestrated spinodal decomposition, offering a rational design methodology to develop for extreme‐condition ceramics.