Project

AI for next-generation fusion materials development

Composite image showing metal panels, microscopy views of material structures, stacked metal sheets and molten metal pouring in a manufacturing facility.
ORNL’s Ying Yang leads the SMART-AI project to advance structural materials for fusion energy. AI will make decisions about processing and operating conditions to speed industrial designs. Image credit: Renee Manning, ORNL

SMART-AI for Scale-Up and MAterial Reliability Translation of Advanced Structural Alloys for Fusion Applications

Fusion power plants will demand structural materials that can withstand extreme temperatures, stress and irradiation over decades of service—and that can be manufactured reliably at industrial scale. 

SMART-AI (Scale-Up and MAterial Reliability Translation AI), led by ORNL, is an AI/ML framework that unites experimental data, computational thermodynamics and structural descriptors to link processing, microstructure and performance. The platform will rapidly integrate diverse datasets, extrapolate to long-term service conditions and identify optimal material states under realistic industrial constraints. Beyond accelerating scale-up, SMART-AI could transform ASME code qualification—today a decades-long testing process—by providing validated predictive tools that speed up the deployment of advanced structural materials for fusion energy.

Partners

  • Argonne National Laboratory
  • University of Tennessee, Knoxville
  • University of Wisconsin–Madison