用图神经网络直接从结构预测准一维自旋系统的磁化行为。
Graph Neural Network Approach to Predicting Magnetization in Quasi-One-Dimensional Ising Systems
- 将自旋系统构型建模为图，用GNN捕捉局部结构与全局对称性。
- 准确复现磁化曲线中的平台、相变点及几何阻挫效应。
- 可替代蒙特卡洛模拟，实现快速高效磁化预测。
我们提出一种基于图的深度学习框架，用于预测准一维伊辛自旋系统的磁性。将晶格几何结构编码为图，通过图神经网络（GNN）结合全连接层进行处理。模型在蒙特卡洛模拟数据上训练，准确再现了磁化曲线的关键特征，包括平台区、临界相变点以及几何阻挫的影响。该方法同时捕捉局部图案与全局对称性，证明了GNN可直接从结构连通性推断磁性行为。所提方法无需额外蒙特卡洛模拟，即可实现磁化的高效预测。
原文摘要 · Abstract (English)
We present a graph-based deep learning framework for predicting the magnetic properties of quasi-one-dimensional Ising spin systems. The lattice geometry is encoded as a graph and processed by a graph neural network (GNN) followed by fully connected layers. The model is trained on Monte Carlo simulation data and accurately reproduces key features of the magnetization curve, including plateaus, critical transition points, and the effects of geometric frustration. It captures both local motifs and global symmetries, demonstrating that GNNs can infer magnetic behavior directly from structural connectivity. The proposed approach enables efficient prediction of magnetization without the need for additional Monte Carlo simulations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。