|About this Abstract
||MS&T23: Materials Science & Technology
||Ceramics for New Generation Nuclear Energy System Application
||AI/ML-assisted Design of Phosphate Nuclear Waste Forms
||James Edward Saal, Vinay Hegde, Sarah Allec, Jincheng Du, Thiruvilla Mahadevan, Jayani Kalahe, Brian Riley, John Vienna, Saehwa Chong
|On-Site Speaker (Planned)
||James Edward Saal
Current disposition pathways for salt wastes from molten salt reactors or used nuclear fuel reprocessing produce waste forms with relatively low halide loading, large volumes, poor stability, and poor durability. Recent work has shown the potential for dehalogenation of the waste salt (recycling half the waste mass) and immobilization in iron phosphate glasses to increase waste loading and decrease volume. While these glass waste forms have promise, alternative fully-optimized ceramic or glass-ceramic waste forms may be adopted, with improved cation loading, thermal stability, and mechanical durability by designing waste forms with dehalogenation in mind. We are using machine learning, artificial intelligence, and physics-based simulation methods to rapidly develop novel ceramic phosphate waste forms for dehalogenation and more secure immobilization of salt waste. We will provide a summary of progress in building waste-related materials property databases, machine learning property models, and using AI to identify promising phosphate waste forms.