Tag
Proposes a novel multilevel graph prompt learning framework for crystal property prediction that captures local chemical semantics and global structural symmetry via node-level and graph-level soft prompts, improving state-of-the-art GNN performance by 3%-15% and enabling cross-property knowledge transfer.