Agentic LLM Reasoning in a Self-Driving Laboratory for Air-Sensitive Lithium Halide Spinel Conductors
Yuxing Fei, Bernardus Rendy, Xiaochen Yang, Junhee Woo, Xu Huang, Chang Li, Shilong Wang, David Milsted, Yan Zeng, Gerbrand Ceder

TL;DR
This paper introduces A-Lab GPSS, an autonomous robotic platform with agentic AI for synthesizing and discovering air-sensitive lithium halide spinel conductors, significantly advancing materials discovery under strict air-free conditions.
Contribution
The work presents a novel integrated robotic and AI platform enabling autonomous synthesis and exploration of air-sensitive inorganic materials, expanding capabilities in solid-state materials discovery.
Findings
Synthesized 352 samples exploring diverse compositions.
Achieved 72% realization of possible metal pairings.
Increased high-quality ionic conductors from 1.33% to 5.33%.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
