基于结构特性对玻璃老化时间的分类：一种机器学习方法
Classifying the age of a glass based on structural properties: A machine learning approach
AI总结：
本文利用监督学习和多层感知机，从玻璃瞬时径向分布函数中提取极微弱的结构老化信号，实现了跨越至少四个时间数量级的玻璃老化时间准确分类，证明了机器学习可建立物理老化玻璃的结构-动力学联系。
摘要：
众所周知，非晶固体的物理老化受动力学性质的显著变化所支配，随着材料变老，动力学性质会发生明显改变。相反，诸如径向分布函数之类的结构性质仅表现出极微弱的老化时间依赖性，通常被认为相对于数值噪声可以忽略不计。在此我们证明，结构上极其微弱的老化时间依赖性变化实际上足以在机器学习的支持下可靠地评估玻璃的老化时间。我们采用一种监督学习方法，基于系统瞬时的径向分布函数来预测玻璃的老化时间。具体而言，我们针对一个被淬火至不同温度的模型玻璃形成体系训练了一个多层感知机，并发现该神经网络能够在至少四个时间数量级范围内准确分类我们系统的老化时间。我们的分析还揭示了哪些结构特征编码了最有用的信息。总体而言，这项工作表明，借助机器学习，确实可以为物理老化玻璃建立一种简单的结构-动力学联系。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
