Multi-Agent-Network-Based Idea Generator for Zinc-Ion Battery Electrolyte Discovery: A Case Study on Zinc Tetrafluoroborate Hydrate-Based Deep Eutectic Electrolytes

Abstract

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 and resource-intensive. In response, this work presents a Large Language Model (LLM)-based multi-agent network that proposes DEE compositions for zinc-ion batteries. By analyzing academic papers from the DEE field, the network identifies innovative, inexpensive, and sustainable Lewis bases to pair with Zn(BF4)2·xH2O. A Zn(BF4)2·xH2O-ethylene carbonate (EC) system demonstrates high conductivity (10.6 mS cm−1) and a wide electrochemical stability window (2.37 V). The optimized electrolyte enables stable zinc stripping/plating, achieves outstanding rate performance (81 mAh g−1 at 5 A g−1), and supports 4000 cycles in Zn||polyaniline cells at 3 A g−1. Spectroscopic analyses and simulations reveal that EC coordinates to Zn2+, mitigating water-induced corrosion, while a fluorine-rich hybrid organic/inorganic solid electrolyte interphase enhances stability. This work showcases a pioneering LLM-driven approach to electrolyte development, establishing a new paradigm in materials research.