  Artificial Intelligence-Generated Content (AIGC)-content autonomously
produced by AI systems without human intervention-has significantly boosted
efficiency across various fields. However, the AIGC in material science faces
challenges in the ability to efficiently discover innovative materials that
surpass existing databases, alongside the invariances and stability
considerations of crystal structures. To address these challenges, we develop
T2MAT (Text-to-Material), a comprehensive framework 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 principal validation. Furthermore, we
propose CGTNet (Crystal Graph Transformer NETwork), a novel graph neural
network model that captures long-term interactions, to enhance the accuracy and
data efficiency of property prediction and thereby improve the reliability of
inverse design. Through these contributions, T2MAT minimizes the dependency on
human expertise and significantly enhances the efficiency of designing novel,
high-performance functional materials, thereby actualizing AIGC in the
materials design domain.
