Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy
This work addresses the difficult problem of magnetic structure prediction for materials scientists, providing a scalable data-driven alternative to costly experiments and first-principles methods.
The authors introduce a graph neural network (MSN) that predicts collinear and non-collinear magnetic structures from atomic coordinates with near-experimental accuracy, using a unified representation for commensurate and incommensurate orders. The model achieves high fidelity in reconstructing experimental magnetic structures.
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.