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

## TL;DR

This paper demonstrates that machine learning can reliably predict the age of a glass from its structural properties, despite their weak dependence on age, by using a neural network trained on radial distribution functions.

## Contribution

It introduces a machine learning approach to classify glass age based on structural data, revealing subtle structural features linked to aging.

## Key findings

- Neural network accurately classifies glass age over four orders of magnitude in time.
- Structural features contain sufficient information to determine the aging state.
- Machine learning establishes a link between structure and dynamics in aged glasses.

## Abstract

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 glassformer quenched to different temperatures, and find that this neural network can accurately classify the age of our system across at least four 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.