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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Reinforcement Learning Recommendations for Crystallization-Resistant Nuclear Waste Glass Formulation

Author(s)

Irmak Sargin

On-Site Speaker (Planned)

Irmak Sargin

Abstract Scope

Nepheline (NaAlSiO₄) crystallization is a persistent challenge in vitrifying high-alumina nuclear waste due to its detrimental effects on residual glass durability. Traditional rules, such as the Nepheline Discriminator (ND), are often conservative and can restrict achievable waste loadings. Building on prior machine learning (ML) advances for nepheline prediction, we introduce a reinforcement learning (RL) framework for autonomous glass optimization. In batch testing, the RL agent identified additive combinations that yielded predicted nepheline formation probability below 0.5 in all tested cases; notably, a more stringent metastability threshold (p < 0.40) was satisfied by 52% of optimized glasses. For each simulated waste stream, the RL agent recommends specific oxide adjustments to achieve formulations that are both experimentally viable and resistant to nepheline crystallization. This methodology highlights the synergy between glass science and machine learning, enabling autonomous, safer, more robust vitrification by reducing the need for human intervention in hazardous environments.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Environmental Effects, Process Technology

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