Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy

2026-05-15

AI Summary

This work addresses the challenge of efficiently and accurately determining complex magnetic structures—particularly noncollinear and incommensurate types—using either experimental techniques or first-principles calculations. The authors propose an end-to-end deep learning framework based on E(3)-equivariant graph neural networks that directly predicts experimentally relevant magnetic order from atomic crystal structures. The key innovation lies in the introduction of a primitive modulated structure representation (PMSR), which enables a unified encoding of both commensurate and incommensurate magnetic configurations without relying on symmetry assumptions. Trained on the MAGNDATA dataset, the model achieves high-fidelity reconstruction across all modulation components, establishing a scalable, data-driven paradigm for the discovery of magnetic materials.

Abstract

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.

Problem

• magnetic structure
• noncollinear order
• incommensurate order
• first-principles methods
• experimental determination

Innovation

• Magnetic Structure Prediction
• E(3) Equivariant Graph Neural Network
• Primitive Modulated Structure Representation
• Noncollinear Magnetism
• Data-Driven Materials Discovery