用原子结构直接预测磁性排列，精度接近实验。
Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy
- 基于图神经网络，从原子坐标直接预测磁结构。
- 在实验数据上重建磁结构，精度高且适用于非共线与非整数周期结构。
- 适合材料发现和磁性材料设计研究者使用。
磁序是材料的基本性质，决定集体行为并赋予多种功能。然而磁结构难以确定：实验成本高且专用，而第一性原理方法常难以处理真实材料中的非共线和非整数周期结构。本文提出磁结构网络（MSN），一种E(3)等变图神经网络，可直接从原子晶体结构预测共线与非共线磁结构，训练数据来自MAGNDATA中的实验结构。通过引入原始调制结构表示（PMSR），我们以统一方式编码整数与非整数周期结构，无需对称性假设。模型在所有调制分量上表现优异，能高保真重构实验磁结构。该方法为快速磁结构预测提供可扩展框架，推动磁性材料的数据驱动发现。
原文摘要 · Abstract (English)
Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic structure remains difficult to determine: experiments are costly and specialized, while first-principles methods often struggle with the noncollinear and incommensurate orders found in real materials. Here we introduce magnetic structure network (MSN), an E(3) equivariant graph neural network that predicts both collinear and non-collinear magnetic structures directly from atomic crystal structures, trained directly on experimentally determined structures from MAGNDATA. By proposing the primitive modulated structure representation (PMSR), we are able to encode commensurate and incommensurate structures in a unified way without symmetry assumptions. The model achieves strong performance across all modulation components and reconstructs experimental magnetic structures with high fidelity. Our approach provides a scalable framework for rapid magnetic structure prediction and opens a route to data-driven discovery of magnetic materials.
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