“Agentic Assistant for Materials Scientists,” by Ruozhu Feng, Yangang Liang, Tianzhixi Yin, Peiyuan Gao, and Wei Wang (ECS Interface 34(2), June 2025), is one of the year’s most-read feature articles. This innovative work showcases the potential of large language models (LLMs) as autonomous yet scientifically rigorous collaborators in materials science. By blending AI-driven function calling with experimental and computational tools, the authors pioneer a future where scientific inquiry gains speed and sophistication. It’s no surprise that this article has resonated widely—offering both a thought-provoking vision and concrete methods for the emerging age of agentic scientific automation. (more…)
