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.

中文翻译：

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根据结构特性对玻璃的年龄进行分类：一种机器学习方法

众所周知，随着材料变老，无定形固体的物理老化是由动态特性的显着变化决定的。相反，诸如径向分布函数之类的结构特性仅表现出非常弱的年龄依赖性，通常被认为相对于数值噪声可以忽略不计。在这里，我们证明，在机器学习的支持下，结构中极其微弱的与年龄相关的变化实际上足以可靠地评估玻璃的年龄。我们采用监督学习方法根据系统的瞬时径向分布函数来预测玻璃的寿命。具体来说，我们为淬火到不同温度的模型玻璃成型器训练多层感知器，发现该神经网络可以在至少 4 个数量级的时间范围内准确地对系统的年龄进行分类。我们的分析还揭示了哪些结构特征编码了最有用的信息。总的来说，这项工作表明，通过机器学习的帮助，确实可以为物理老化的眼镜建立简单的结构-动力学联系。