August 11, 2025
5 min read
Chris Randall
University of Bayreuth Develops AI Multi-Agent System to Accelerate Early Battery Research
Researchers at the University of Bayreuth and the Hong Kong University of Science and Technology have pioneered an AI-based multi-agent system to rapidly develop new electrolytes, significantly shortening the initial stages of battery research. According to the University of Bayreuth, the new AI tool enables the generation of suggestions for new battery materials much faster than traditional methods. Currently, identifying suitable materials is a lengthy and resource-intensive process: “Promising material compositions must first be found and then experimentally tested – a process that often takes weeks or even months,” say the project managers. The new AI approach achieves the same result in just a few hours. The international research team recently published their findings in the journal Advanced Materials under the title: “Multi-Agent-Network-Based Idea Generator for Zinc-Ion Battery Electrolyte Discovery: A Case Study on Zinc Tetrafluoroborate Hydrate-Based Deep Eutectic Electrolytes.” Specifically, the Bayreuth researchers, in collaboration with the Hong Kong University of Science and Technology, developed a multi-agent system based on large language models (LLMs) such as ChatGPT. This system consists of two specialized software agents that collaborate to solve research questions. One agent maintains a broad overview of the available literature, while the other possesses in-depth, detailed expertise. This collaboration forms a groundbreaking approach to accelerating material discovery. “Our new multi-agent system acts as a creative scientific partner with two specialised agents that analyse relevant literature,” summarizes Prof. Dr. Francesco Ciucci from the Chair of Electrode Design for Electrochemical Energy Storage at the Bavarian Centre for Battery Technology (BayBatt) at the University of Bayreuth. “Through a subsequent simulation of a scientific debate, the two agents combine ideas from their extensive training data and the literature to propose novel electrolyte compositions.” Dr. Matthew J. Robson from the Hong Kong University of Science and Technology adds: “The most important thing here is the development of the role of AI in the scientific process. We have designed a blueprint for scientific research that transforms AI from a passive tool for data analysis into an active, creative partner that can generate truly novel and high-quality hypotheses.” The researchers tested their approach in practice: the multi-agent system proposed several novel, cost-effective, and environmentally friendly electrolyte components for zinc batteries. “One of the electrolytes demonstrated outstanding performance in experimental testing, rivalling the most advanced systems in its electrolyte class,” the researchers report. The new design has proven its outstanding durability through more than 4,000 charge and discharge cycles. It also set a new fast-charging record in its electrolyte class and achieved almost 20% higher capacity at fast-charging speeds compared to similar electrolytes. Prof. Ciucci emphasizes the potential impact: “Combined with validation through laboratory experiments and the critical judgment of researchers, promising AI suggestions could lead to faster solutions to global challenges.”For more details, visit the University of Bayreuth press release.
Source: electrive.com by Chris Randall