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

G Janzen and C Smit and S Visbeek and VE Debets and CJ Luo and C Storm and S Ciarella and LMC Janssen, PHYSICAL REVIEW MATERIALS, 8, 025602 (2024).

DOI: 10.1103/PhysRevMaterials.8.025602

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.

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