| Abstract Scope |
Multi-principal element alloys show promise for nuclear applications requiring resistance to extreme temperature, irradiation, and corrosion, yet progress is limited by sparse experimental data across vast composition spaces. This work presents a high-throughput methodology integrating thermodynamic design, additive manufacturing, and automated characterization to accelerate alloy discovery. Directed energy deposition enables fabrication of up to 25 bulk compositions (1 cm³ each) per day. Batch heat treatment and metallography are combined with automated X ray diffraction, indentation, corrosion, and irradiation testing. Active machine learning models predict properties and guide composition down-selection. Scalability to component-level production via laser powder bed fusion is enabled using reduced-order energy balance models. Case studies on Cr–Fe–Mn–Ni and W–Ta–Cr–V alloys demonstrate the flexibility and translational potential of this approach. Overall, the methodology reduces alloy discovery timelines from years to months while achieving properties that meet or exceed current nuclear materials benchmarks. |