Agentic Assistant for Material Scientists

With the fast-paced advancement of artificial intelligence (AI), emerging applications of large language models (LLMs) have demonstrated useful applications in material science and self-driving labs (SDLs). Lacking understanding of the relationship between the probability distribution of an LLM and token output, especially in a scientific setting, some concerns are maintaining scientific rigor and autonomy when integrating AI tools. In this correspondence, we propose to use LLM with function calling as an automation node. Such automation can be virtual automation or experimental automation. The proposed agentic automation would project a balance between an elevated level of autonomy in the automation process while maintaining scientific rigor by acquiring data through traditional computing software or experimental tools.