Classifying the age of a glass based on structural properties: A machine learning approach

Giulia Janzen, Casper Smit, Samantha Visbeek, Vincent E. Debets, Chengjie Luo, Cornelis Storm, Simone Ciarella (Corresponding author), Liesbeth M.C. Janssen (Corresponding author)

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It is well established that physical aging of amorphous solids is governed by a marked change in dynamical properties as the material becomes older. Conversely, structural properties such as the radial distribution function exhibit only a very weak age dependence, usually deemed negligible with respect to the numerical noise. Here we demonstrate that the extremely weak age-dependent changes in structure are, in fact, sufficient to reliably assess the age of a glass with the support of machine learning. We employ a supervised learning method to predict the age of a glass based on the system's instantaneous radial distribution function. Specifically, we train a multilayer perceptron for a model glass former quenched to different temperatures and find that this neural network can accurately classify the age of our system across at least 4 orders of magnitude in time. Our analysis also reveals which structural features encode the most useful information. Overall, this work shows that through the aid of machine learning, a simple structure-dynamics link can, indeed, be established for physically aged glasses.

Original languageEnglish
Article number025602
Number of pages10
JournalPhysical Review Materials
Issue number2
Publication statusPublished - Feb 2024


It is a pleasure to thank R. Jack for stimulating discussions. This work has been financially supported by the Dutch Research Council (NWO) through a START-UP grant (V.E.D., C.L., and L.M.C.J.), Physics Projectruimte grant (G.J. and L.M.C.J.), and Vidi grant (L.M.C.J.).

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek


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