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

Title data

Robson, Matthew J. ; Xu, Shengjun ; Wang, Zilong ; Chen, Qing ; Ciucci, Francesco:
Multi-Agent-Network-Based Idea Generator for Zinc-Ion Battery Electrolyte Discovery : A Case Study on Zinc Tetrafluoroborate Hydrate-Based Deep Eutectic Electrolytes.
In: Advanced Materials. Vol. 37 (2025) Issue 32 . - 2502649.
ISSN 1521-4095
DOI: https://doi.org/10.1002/adma.202502649

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Abstract in another language

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.