论文标题
Botan：通过机器学习相对运动预测慢速玻璃动力学的债券目标网络
BOTAN: BOnd TArgeting Network for prediction of slow glassy dynamics by machine learning relative motion
论文作者
论文摘要
机器学习的最新发展使得对玻璃形成系统中缓慢结构放松的动态进行了准确的预测。但是，这些任务的现有机器学习模型的设计主要是为了学习一个动态数量，并将其与玻璃液体的结构特征相关联。在这项研究中，我们提出了一个图形神经网络模型``键靶网络（botan）''，该模型除了粒子的自我运动外，还了解相邻粒子之间相对运动的相对运动。通过将结构特征与这两个不同的动态变量联系起来，该模型自主获得了识别不同动力学过程，应变波动和粒子重排的能力，会影响缓慢放松缓慢的颗粒的自我运动，从而可以高精度地预测空间和时间慢的结构放松如何发展。
Recent developments in machine learning have enabled accurate predictions of the dynamics of slow structural relaxation in glass-forming systems. However, existing machine-learning models for these tasks are mostly designed such that they learn a single dynamic quantity and relate it to the structural features of glassy liquids. In this study, we propose a graph neural network model, ``BOnd TArgeting Network (BOTAN)'', that learns relative motion between neighboring pairs of particles, in addition to the self-motion of particles. By relating the structural features to these two different dynamical variables, the model autonomously acquires the ability to discern how different dynamical processes, strain fluctuations and particle rearrangements, affect the self-motion of particles undergoing slow relaxation, and thus can predict with high precision how slow structural relaxation develops in space and time.
