# Predicting the dissolution kinetics of silicate glasses using machine learning Source: [Predicting the dissolution kinetics of silicate glasses using machine learning]() Collection index: [llms.txt]() Data updated: 2025-10-24T22:19:51.381Z Authors: N. M. Anoop Krishnan, Sujith Mangalathu, Morten M. Smedskjaer, Adama Tandia, Henry Burton, Mathieu Bauchy Published: 2017-12-16T20:46:40.000Z ## Abstract Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()