Symm-CGNN: Symmetry-Information-Enhanced Crystal Graph Neural Network for High-Symmetry Point Band Gap Prediction

Accurately characterizing the anisotropic optoelectronic properties of crystals requires determining the band gaps at specific high-symmetry points in the Brillouin zone. Relying solely on the minimum band gap is insufficient. However, although Graph Neural Networks (GNNs) offer rapid property predictions, conventional models remain trapped in a local real-space paradigm, lacking the global symmetry information necessary to differentiate these high-symmetry energy states. To address this problem, we propose the Symmetry-Information-Enhanced Crystal Graph Neural Network (Symm-CGNN). It explicitly incorporates local atomic environments with global symmetry information, including space groups, crystal systems, material density and lattice constants. The evaluation is performed on a comprehensive dataset including 3D (Materials Project) and 2D (2DMatpedia). The results demonstrate that Symm-CGNN achieves an 18% reduction in Mean Absolute Error (MAE) for high-symmetry band gap prediction compared to the baseline Crystal Graph Convolutional Neural Networks (CGCNN). This approach bridges the representational gap between local atomic coordination and macroscopic symmetry. Consequently, it provides a robust and efficient machine-learning paradigm for the high-throughput screening of materials with anisotropic optoelectronic properties.