Magnetic Structure Network (MSN)

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

The paper introduces MSN (Magnetic Structure Network), an E(3) equivariant graph neural network that predicts collinear and non-collinear magnetic structures directly from atomic crystal coordinates. Trained on experimentally determined structures from the MAGNDATA database, it uses a novel Primitive Modulated Structure Representation (PMSR) to handle both commensurate and incommensurate magnetic orders in a unified framework without symmetry assumptions. The model achieves near-experimental accuracy across diverse magnetic structure types, offering a scalable alternative to costly experiments and computationally demanding first-principles methods for magnetic materials discovery.