Integration of high-throughput experiments and physics-informed machine learning to understand irradiation-induced swelling in metallic glass

Structural materials in nuclear power systems commonly experience degradation of mechanical properties due to radiation-induced swelling and damage. Metallic glasses, however, represent a unique class of structural alloys that combine the favorable properties of metals with a non-crystalline, glass-like atomic structure. Metallic glasses have demonstrated excellent resistance to radiation damage, and, remarkably, some even show improved mechanical properties under radiation exposure. Thus, metallic glasses are promising candidates for safer, more reliable structural materials in nuclear power applications. While irradiation-induced swelling in crystalline materials is well-characterized through established thermodynamic and kinetic models of crystallographic defects, the corresponding mechanisms in metallic glasses are far less understood because it is inherently challenging to define and characterize 'defects' within a disordered atomic structure. The objectives of this research are to investigate how radiation influences metallic glasses at the atomic level, to elucidate the processes that lead to material swelling under irradiation, and to develop predictive models that can anticipate these effects. The research approach integrates high-throughput experimentation, state-of-the-art electron microscopy methods, computer simulations at the atomic scale, and physics-guided artificial intelligence techniques. By leveraging this comprehensive, multi-technique strategy, the project will establish an atomic-level, data-driven understanding of radiation-induced defects in metallic glasses. Insights gained from this project may accelerate the development of advanced nuclear materials that are more resistant to radiation damage, thereby enhancing the safety and performance of future nuclear energy systems.