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Author

Xiao-Nan Lu

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Open access Sep 2026

Machine Learning Enabled Prediction and Interpretation of the Initial Dissolution Rates of Nuclear Waste Glasses

Accurate prediction of chemical durability, such as dissolution rate, is crucial for glass materials used in diverse applications, including pharmaceutical packaging and nuclear waste disposal. In this work, machine learning‐based prediction models were developed using structural descriptors derived from molecular...

Wen-Qing Xie, Jayani Kalahe, Kenneth Sanders et al. · 0 citations
Aug 2026

Atomistic Simulation of Glasses and Amorphous Materials: Challenges and Opportunities for the Next Decade

This perspective examines three interconnected issues, namely, glass formation procedures, interatomic potential development, and machine learning applications, which emerged from the 5th International Workshop on Challenges of Atomistic Simulations of Glasses and Amorphous Materials.

N. A. Anoop Krishnan, A. Pedone, Xiao-Nan Lu et al. · 0 citations

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