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Meeting MS&T22: Materials Science & Technology
Symposium Progressive Solutions to Improve Corrosion Resistance of Nuclear Waste Storage Materials
Presentation Title Predicting the Long-term Durability of Nuclear Waste Immobilization Glasses using Machine Learning
Author(s) Mathieu Bauchy
On-Site Speaker (Planned) Mathieu Bauchy
Abstract Scope The long-term durability of glasses is a key performance metric for nuclear waste immobilization applications. Although some models have been proposed to predict the short-term forward dissolution kinetics of glasses, long-term dissolution is a more complex behavior that is influenced by the glass structure, the feedback from the solution, and the precipitation of secondary phases. This complexity has thus far limited our ability to robustly predict the long-term dissolution rate of nuclear waste immobilization glasses. Here, based on the analysis of a large dataset of vapor hydration tests (VHT), we develop using machine learning a Gaussian Process Regression (GPR) model to predict the long-term durability of glasses. Importantly, GPR models are non-parametric and intrinsically capture the uncertainty of the prediction. We demonstrate that our GPR model features excellent accuracy. This model allows us to decipher the propensity for each oxide to accelerate or slow down the dissolution kinetics of glasses.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Designing Glasses for Nuclear Waste Immobilization with AI and ML
Diminished Diffusion in the Aged Hydrated Gels of Irradiated Borosilicate Glasses
Environmental Cracking Lifetime Prediction through the Development of Pitting and SCC Models for Nuclear Waste Storage Casks
From Preferential Bonding to Phase Separation in Boro-silicate Glasses
Microstructural Development and Chemical Durability of a Borosilicate Glass-ceramic Waste-form
Predicting the Long-term Durability of Nuclear Waste Immobilization Glasses using Machine Learning
SCC of Nuclear Waste Canisters: Mechanisms and Mitigation

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