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

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中图分类号：064 doi：10.1016/j.actphy.2025.100213

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

Zhilong Song 1.2， Shuaihua Lu 1， Qionghua Zhou 1，2，* ， Jinlan Wang 1.，2*

1 Key Laboratory of Quantum Materials and Devices of Ministry of Education， School of Physics， Southeast University， Nanjing 211189， Jiangsu Province， China.

² Suzhou Laboratory， Suzhou 215o04， Jiangsu Province， China.

Abstract：Artificial Intelligence-Generated Content （AIGC）—content autonomously produced by AI systems without human intervention—has significantly boosted eficiency 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 eficiency 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.

Key Words： Agent;Material design; Large language model; Generative model; Graph neural network

1引言

人工智能生成内容（AIGC）领域的最新进展引发了一场深刻的跨领域革命，使高效、高质量的AI内容生成达到前所未有的水平[1]。（剩余20437字）