Schoenholz Samuel S, Cubuk Ekin D, Kaxiras Efthimios, Liu Andrea J
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104.
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
Proc Natl Acad Sci U S A. 2017 Jan 10;114(2):263-267. doi: 10.1073/pnas.1610204114. Epub 2016 Dec 27.
The dynamical glass transition is typically taken to be the temperature at which a glassy liquid is no longer able to equilibrate on experimental timescales. Consequently, the physical properties of these systems just above or below the dynamical glass transition, such as viscosity, can change by many orders of magnitude over long periods of time following external perturbation. During this progress toward equilibrium, glassy systems exhibit a history dependence that has complicated their study. In previous work, we bridged the gap between structure and dynamics in glassy liquids above their dynamical glass transition temperatures by introducing a scalar field called "softness," a quantity obtained using machine-learning methods. Softness is designed to capture the hidden patterns in relative particle positions that correlate strongly with dynamical rearrangements of particle positions. Here we show that the out-of-equilibrium behavior of a model glass-forming system can be understood in terms of softness. To do this we first demonstrate that the evolution of behavior following a temperature quench is a primarily structural phenomenon: The structure changes considerably, but the relationship between structure and dynamics remains invariant. We then show that the relaxation time can be robustly computed from structure as quantified by softness, with the same relation holding both in equilibrium and as the system ages. Together, these results show that the history dependence of the relaxation time in glasses requires knowledge only of the softness in addition to the usual state variables.
动力学玻璃化转变通常被认为是玻璃态液体在实验时间尺度上不再能够达到平衡的温度。因此，这些系统在动力学玻璃化转变温度之上或之下的物理性质，比如粘度，在受到外部扰动后的很长一段时间内可能会改变多个数量级。在向平衡态转变的过程中，玻璃态系统表现出历史依赖性，这使得对它们的研究变得复杂。在之前的工作中，我们通过引入一个名为“柔软度”的标量场弥合了高于其动力学玻璃化转变温度的玻璃态液体中结构与动力学之间的差距，“柔软度”是一个使用机器学习方法获得的量。柔软度旨在捕捉相对粒子位置中的隐藏模式，这些模式与粒子位置的动力学重排密切相关。在这里，我们表明一个模型玻璃形成系统的非平衡行为可以用柔软度来理解。为此，我们首先证明温度猝灭后行为的演变主要是一种结构现象：结构发生了相当大的变化，但结构与动力学之间的关系保持不变。然后我们表明，弛豫时间可以根据由柔软度量化的结构可靠地计算出来，在平衡态和系统老化过程中都存在相同的关系。总之，这些结果表明，玻璃中弛豫时间的历史依赖性除了通常的状态变量外，只需要知道柔软度即可。
