物理化学学报2026，Vol.42Issue(5)：127-139,13.DOI:10.1016/j.actphy.2025.100213
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
T2MAT(text-to-material):a universal agent for generating material structures with goal properties from a single sentence
摘要
Abstract
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.关键词
智能体/材料设计/大语言模型/生成式模型/图神经网络Key words
Agent/Material design/Large language model/Generative model/Graph neural network分类
化学化工引用本文复制引用
宋志龙,陆帅华,周跫桦,王金兰..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[J].物理化学学报,2026,42(5):127-139,13.基金项目
本研究得到国家重点研发计划(2022YFA1503103,2021YFA1500703) （2022YFA1503103,2021YFA1500703）
国家自然科学基金(22033002,92261112,22373013)以及江苏省基础研究计划(BK20232012,BK20222007)的资助.感谢东南大学大数据计算中心为本研究提供的计算设施支持. （22033002,92261112,22373013）
