用智能机器人在无氧环境合成锂卤化物尖晶石，自动探索新材料。
Agentic LLM Reasoning in a Self-Driving Laboratory for Air-Sensitive Lithium Halide Spinel Conductors
- 用智能体推理设计实验，结合归纳与溯因策略。
- 352个样品中实现72%的金属组合，导电率超0.05 mS/cm的占比从1.33%升至5.33%。
- 适合材料自动化、无氧合成和智能科研平台研究者参考。
自驱动实验室有望加速材料发现。然而现有自动化固态合成平台仅限于常压条件，无法用于对空气敏感的材料。本文提出A-Lab GPSS（手套箱粉末固态合成自驱动实验室），一个可在严格无氧条件下合成与表征空气敏感无机材料的机器人平台。通过将智能体人工智能框架集成至A-Lab GPSS，系统以溯因与归纳推理结构化地实现自主实验设计。我们利用该平台探索锂卤化物尖晶石固态离子导体的广阔组成空间。在包含352个不同组成的合成实验中，系统实现了19种金属间171种可能二元组合中的72%。在整个实验过程中，同时具备高离子电导率（> 0.05 mS/cm）与高卤化物尖晶石相纯度的成分比例，从最初75个智能体提议样品中的1.33%提升至最后75个的5.33%。通过分析AI推理过程，揭示出两种互补策略：溯因推理聚焦已探索区域内的异常现象，归纳推理则拓展至此前未访问的更广化学空间。本工作建立了一个可扩展的平台，用于复杂空气敏感固态材料的自主发现。
原文摘要 · Abstract (English)
Self-driving laboratories promise to accelerate materials discovery. Yet current automated solid-state synthesis platforms are limited to ambient conditions, thereby precluding their use for air-sensitive materials. Here, we present A-Lab for Glovebox Powder Solid-state Synthesis (A-Lab GPSS), a robotic platform capable of synthesizing and characterizing air-sensitive inorganic materials under strict air-free conditions. By integrating an agentic AI framework into the A-Lab GPSS platform, we structure autonomous experimental design through abductive and inductive reasoning. We deploy this platform to explore the vast compositional space of lithium halide spinel solid-state ionic conductors. Across a synthesis campaign comprising 352 samples with diverse compositions, the system explores a broad chemical space, experimentally realizing 72% of the 171 possible pairwise combinations among the 19 metals considered in this study. Over the course of the campaign, the fraction of compositions exhibiting both good ionic conductivity (> 0.05 mS/cm) and high halide spinel phase purity increases from 1.33% in the first 75 agent-proposed samples to 5.33% in the final 75. Furthermore, by inspecting the AI's reasoning processes, we reveal distinct yet complementary discovery strategies: abductive reasoning interrogates abnormal observations within already explored regions, whereas inductive reasoning expands the search into broader, previously unvisited chemical space. This work establishes a scalable platform for the autonomous discovery of complex, air-sensitive solid-state materials.
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