用自然语言自动发现高性能材料，还能提炼出可解释的设计规律。
From Natural Language to Materials Discovery:The Materials Knowledge Navigation Agent
- 通过自然语言指令自动执行数据检索、性质预测和结构生成
- 发现高德拜温度陶瓷，提出新稳定硼碳化合物候选
- 适合材料科学家快速探索未知领域，无需编程
加速高性能材料发现仍是能源、电子和航空航天技术中的核心挑战，传统流程高度依赖专家直觉和计算成本高昂的模拟。本文提出材料知识导航代理（MKNA），一种以语言驱动的系统，能将自然语言科学意图转化为数据库检索、性质预测、结构生成与稳定性评估等可执行动作。除了自动化工具调用，MKNA还能从文献和数据库中自主提取定量阈值与化学上有意义的设计模式，实现数据驱动的假说构建。应用于高德拜温度陶瓷搜索时，该代理识别出文献支持的筛选标准（Theta_D > 800 K），重新发现金刚石、SiC、SiN和BeO等经典超硬材料，并提出热力学稳定的新型硼碳富集化合物，填补了1500–1700 K区域的空白。结果表明，MKNA不仅能发现稳定候选物，还能重构可解释的设计准则，建立了一个通用的、语言引导的自主材料探索平台。
原文摘要 · Abstract (English)
Accelerating the discovery of high-performance materials remains a central challenge across energy, electronics, and aerospace technologies, where traditional workflows depend heavily on expert intuition and computationally expensive simulations. Here we introduce the Materials Knowledge Navigation Agent (MKNA), a language-driven system that translates natural-language scientific intent into executable actions for database retrieval, property prediction, structure generation, and stability evaluation. Beyond automating tool invocation, MKNA autonomously extracts quantitative thresholds and chemically meaningful design motifs from literature and database evidence, enabling data-grounded hypothesis formation. Applied to the search for high-Debye-temperature ceramics, the agent identifies a literature-supported screening criterion (Theta_D > 800 K), rediscovers canonical ultra-stiff materials such as diamond, SiC, SiN, and BeO, and proposes thermodynamically stable, previously unreported Be-C-rich compounds that populate the sparsely explored 1500-1700 K regime. These results demonstrate that MKNA not only finds stable candidates but also reconstructs interpretable design heuristics, establishing a generalizable platform for autonomous, language-guided materials exploration.
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