T2MAT(文本到材料):基于文本生成目标性能材料结构的通用智能体OA

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 ensuring the invariance and stability of crystal structures.To address these challenges,we develop T2MAT(text-to-material),an end-to-end agent that transforms user-input text into the inverse design of novel material structures with target properties beyond existing database,enabled by comprehensive exploration of chemical space and fully automated first-principles validation.Furthermore,we propose CGTNet(Crystal Graph Transformer NETwork),a graph neural network specifically designed to capture long-range interactions,which dramatically improves the accuracy and data efficiency of property predictions and thereby strengthens the reliability of inverse design.Through these contributions,T2MAT reduces the reliance on human expertise and accelerates the discovery of high-performance functional materials,paving the way for truly autonomous material design.