ABSTRACT 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 dynamics (MD) simulations and
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