T2MAT (text-to-materials): A universal agent for generating material structures with goal properties from a single sentence

Artificial Intelligence-Generated Content (AIGC)-content autonomously produced by AI systems without human intervention-has significantly boosted efficiency across various fields. However, AIGC in material science faces challenges in efficiently discovering novel materials that surpass existing databases, while simultaneously addressing the invariance and stability of crystal structures. To address these challenges, we develop T2MAT (text-to-material), a comprehensive agent processing from a user-input sentence to inverse design material structures with goal properties beyond the existing database via globally exploring chemical space, followed by an entirely automated workflow of first-principles validation. Furthermore, we propose CGTNet (Crystal Graph Transformer NETwork), a graph neural network model that captures long-range interactions, to enhance the accuracy and data utilization efficiency of property prediction and thereby strengthen the reliability of inverse design. Through these contributions, T2MAT minimizes the dependency on human expertise and significantly improves the efficiency of discovering novel, high-performance functional materials, offering a robust way toward more autonomous materials design.