Multi-agent-network-based idea generator for zinc-ion battery electrolyte discovery

Aqueous deep eutectic electrolytes (DEEs) offer great potential for low-cost zinc-ion batteries but often have limited performance. Discovering new electrolytes is, therefore, crucial, yet time-consuming. To address this, this work presents a Large Language Model (LLM)-based multi-agent network that proposes DEE compositions for zinc-ion batteries. Analyzing academic papers from the DEE field, the network identified innovative, inexpensive, and sustainable Lewis bases to pair with Zn(BF4)2.xH2O. A Zn(BF4)2.xH2O-ethylene carbonate (EC) system demonstrated high conductivity (10.6 mS cm-1) and a wide electrochemical stability window (2.37 V). The optimized electrolyte enabled stable zinc stripping/plating, achieved outstanding rate performance (81 mAh g-1 at 5 A g-1), and supported 4000 cycles in Zn||polyaniline cells at 3 A g-1. Spectroscopic analyses and simulations revealed that EC coordinates to Zn2+, mitigating water-induced corrosion, while a fluorine-rich hybrid organic/inorganic solid electrolyte interphase enhances cycling stability. This work showcases a pioneering LLM-driven approach to electrolyte development, establishing a new paradigm in materials research.