论文标题
几何增强图形神经网络，用于学习静态结构的玻璃动力学平滑度
Geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure
论文作者
论文摘要
了解玻璃系统的动态过程仍然具有挑战性。最近的进步表明，图神经网络（GNNS）在确定玻璃系统中结构与动力学之间的相关性方面的功能。这些方法将玻璃系统视为拓扑图。但是，忽略了图表上动力学的固有“平滑度”属性（动力学上的变化），从而导致GNN的性能恶化，以实现各个时间尺度上的动态预测。在本文中，我们首先提出了一项实验研究，该研究从图表的角度评估了粒子动力学的平滑度模式。结果表明，长期动力学在图上表现出更平滑的属性，而短时动力学则显示出明显的非平滑模式。为了建立具有不同平滑度模式的静态结构与动力学之间的关系，我们提出了一种新颖的几何形状增强图神经网络（GEO-GNN）模型。 GEO-GNN体系结构由几何特征编码器组成，该编码器结合了旋转不变的距离和角度，以增强几何特征学习和几何增强的聚合块，可以在消息传递过程中自适应地学习潜在的平滑度模式。实验结果表明，我们的方法能够捕获粒子的固有平滑度模式，并且在预测所有时间尺度的动力学方面都超过了最先进的基准。随后的研究表明，几何特征对于模型准确捕获动态中的平滑度模式至关重要。我们的研究不仅完善了预测玻璃动力学的方法，而且还提供了一种新的镜头，可以通过该镜头研究玻璃中有关动态异质性的因果关系问题。
Understanding the dynamic processes of the glassy system continues to be challenging. Recent advances have shown the power of graph neural networks (GNNs) for determining the correlation between structure and dynamics in the glassy system. These methods treat the glassy system as a topological graph. However, the inherent "smoothness" property of the dynamics on the graph (variation of dynamics over the graph) is ignored, resulting in a deteriorated performance of GNN for dynamic predictions over various time scales. In this paper, we first present an experimental investigation assessing the smoothness patterns of particle dynamics from the graph perspective. The results indicate that the long-time dynamics exhibit a smoother property on the graph, while the short-time dynamics reveal a distinctly non-smooth pattern. To establish the relationship between the static structure and dynamics with different smoothness patterns, we propose a novel geometry-enhanced graph neural network (Geo-GNN) model. The Geo-GNN architecture consists of a geometry feature encoder that incorporates rotation-invariant distances and angles to enhance geometric feature learning and a geometry-enhanced aggregation block that can adaptively learn the underlying smoothness patterns during message passing. Experimental results demonstrate that our method is able to capture the inherent smoothness pattern of particles and outperforms state-of-the-art baselines in predicting the dynamics across all time scales. A subsequent study has revealed that geometric features are critical for the model to accurately capture smoothness patterns in dynamics. Our research not only refines the method for predicting glassy dynamics but also provides a new lens through which to investigate the issue of causality regarding dynamical heterogeneity in glasses.
