Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy
This paper introduces the Magnetic Structure Network (MSN), an E(3) equivariant graph neural network trained on experimental data that utilizes a novel primitive modulated structure representation to accurately predict both collinear and non-collinear magnetic structures directly from atomic coordinates, overcoming limitations of traditional first-principles methods.