Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy
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 a...
| Authors | Abhijatmedhi Chotrattanapituk, Ryotaro Okabe, Eunbi Rha et al. |
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| Primary category | cond-mat.mtrl-sci |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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