论文标题
比较用于预测玻璃动力学的机器学习技术
Comparing machine learning techniques for predicting glassy dynamics
论文作者
论文摘要
为了了解结构和动力学是如何在眼镜中连接的，已经开发了许多基于机器学习的方法来预测超冷液体中的动力学。这些方法包括日益复杂的机器学习技术，以及用于描述粒子周围环境的越来越复杂的描述符。在许多情况下，所选的机器学习技术和结构描述符的选择都是同时变化的，因此很难定量比较不同机器学习方法的性能。在这里，我们使用三种不同的机器学习算法（线性回归，神经网络和GNN）来预测Boattini等人最近引入的结构输入的玻璃状二元硬球混合物的动态倾向。 [物理。莱特牧师。 127，088007（2021）]。如我们所示，当使用这些高级描述符时，所有三种方法都以几乎相等的精度预测动力学。但是，线性回归是训练速度更快的数量级，使其成为迄今为止选择的方法。
In the quest to understand how structure and dynamics are connected in glasses, a number of machine learning based methods have been developed that predict dynamics in supercooled liquids. These methods include both increasingly complex machine learning techniques, and increasingly sophisticated descriptors used to describe the environment around particles. In many cases, both the chosen machine learning technique and choice of structural descriptors are varied simultaneously, making it hard to quantitatively compare the performance of different machine learning approaches. Here, we use three different machine learning algorithms -- linear regression, neural networks, and GNNs -- to predict the dynamic propensity of a glassy binary hard-sphere mixture using as structural input a recursive set of order parameters recently introduced by Boattini et al. [Phys. Rev. Lett. 127, 088007 (2021)]. As we show, when these advanced descriptors are used, all three methods predict the dynamics with nearly equal accuracy. However, the linear regression is orders of magnitude faster to train making it by far the method of choice.
