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.

翻译：加速高性能材料的发现仍然是能源、电子和航空航天技术领域的核心挑战，传统工作流程在很大程度上依赖于专家直觉和计算成本高昂的模拟。本文介绍材料知识导航智能体（MKNA），这是一个语言驱动的系统，能够将自然语言描述的科学意图转化为可执行的操作，用于数据库检索、性质预测、结构生成和稳定性评估。除了自动化工具调用之外，MKNA 还能从文献和数据库证据中自主提取定量阈值和具有化学意义的设计基元，从而形成基于数据的假设。在应用于寻找高德拜温度陶瓷的案例中，该智能体识别出一个文献支持的筛选标准（Θ_D > 800 K），重新发现了金刚石、SiC、SiN 和 BeO 等经典的超硬材料，并提出了热力学稳定、此前未报道过的富 Be-C 化合物，这些化合物填补了探索稀少的 1500-1700 K 温区。这些结果表明，MKNA 不仅能发现稳定的候选材料，还能重建可解释的设计启发式规则，从而为自主的、语言引导的材料探索建立了一个可推广的平台。