mCGCNN: A Dual-Stream Crystal Graph Convolutional Neural Network for the Efficient Prediction of Magnetic Properties of Crystalline Materials

Abstract

Magnetic order in crystals is governed by moment-carrying sublattices and ligand-mediated exchange pathways, yet standard crystal graph neural networks treat all atoms homogeneously and encode bonds primarily through pair distances. We propose mCGCNN, a magnetism-aware crystal graph network that augments the full structural graph with a dedicated magnetic subgraph. The magnetic stream performs angle-aware message passing over magnetic centers using metal-ligand-metal exchange-path descriptors motivated by Goodenough-Kanamori-Anderson physics, while layer-wise cross-coupling transfers structural and ligand-field information from the full crystal graph. A separate magnetic-sublattice pooling operation prevents the magnetic interaction from being diluted by nonmagnetic atoms. Benchmarked on a curated Materials Project spin-polarized DFT data, mCGCNN improves total magnetic moment prediction from a CGCNN test MAE of 2.54~$μ_B$ to 2.02~$μ_B$, outperforming a strengthened CGCNN readout baseline and raising the test $R^2$ from 0.644 to 0.776. When pretrained on moment regression, the same magnetic representation improves ferromagnetic/antiferromagnetic classification. The results demonstrate that incorporating exchange geometry directly into graph architectures provides a physically grounded route to predictive models of magnetic materials.