论文标题
玻璃液体中的脆弱性：一种基于机器学习的结构方法
Fragility in Glassy Liquids: A Structural Approach Based on Machine Learning
论文作者
论文摘要
过冷时粘度或放松时间的快速上升是玻璃液体的通用标志。然而，粘度的温度依赖性对于玻璃液体而言是非常普遍的，其特征是系统的“脆弱性”，其液体几乎具有Arrhenius温度依赖性弛豫时间，称为强液体，而具有超级Arrhenius行为的液体称为脆弱液体。使某些液体结实而其他易碎的原因仍然不太了解。在这里，我们在一个玻璃状液体家族中探索这个问题，玻璃液体的含量从极强到极脆弱，使用“柔软度”，这是通过机器学习确定的结构顺序参数，该参数与动态重排高度相关。我们使用支撑矢量机将柔软度确定为整个研究液体中结构量的线性组合。然后，我们使用柔软度来确定控制脆弱性的因素。
The rapid rise of viscosity or relaxation time upon supercooling is universal hallmark of glassy liquids. The temperature dependence of the viscosity, however, is quite non universal for glassy liquids and is characterized by the system's "fragility," with liquids with nearly Arrhenius temperature-dependent relaxation times referred to as strong liquids and those with super-Arrhenius behavior referred to as fragile liquids. What makes some liquids strong and others fragile is still not well understood. Here we explore this question in a family of glassy liquids that range from extremely strong to extremely fragile, using "softness," a structural order parameter identified by machine learning to be highly correlated with dynamical rearrangements. We use a support vector machine to identify softness as the same linear combination of structural quantities across the entire family of liquids studied. We then use softness to identify the factors controlling fragility.
