This paper explores the potential of Large Language Models (LLMs) in materials science research. LLMs, with their impressive natural language capabilities and ability to handle ambiguous requirements, are argued to be versatile tools that can accelerate and unify exploration across various domains. The authors examine the basic theory of LLMs, including attention mechanisms and transformers, and discuss their capabilities in processing unstructured and varied data. Two case studies are presented: one on automating 3D microstructure analysis and another on extracting labels for micrographs from scientific papers. The paper highlights the benefits of LLMs in materials science, such as their ability to act as high-level managers and coordinate other systems, and addresses challenges like hallucinations and cost. The authors conclude that while LLMs have significant potential, they should be used judiciously and in workflows that minimize their limitations.This paper explores the potential of Large Language Models (LLMs) in materials science research. LLMs, with their impressive natural language capabilities and ability to handle ambiguous requirements, are argued to be versatile tools that can accelerate and unify exploration across various domains. The authors examine the basic theory of LLMs, including attention mechanisms and transformers, and discuss their capabilities in processing unstructured and varied data. Two case studies are presented: one on automating 3D microstructure analysis and another on extracting labels for micrographs from scientific papers. The paper highlights the benefits of LLMs in materials science, such as their ability to act as high-level managers and coordinate other systems, and addresses challenges like hallucinations and cost. The authors conclude that while LLMs have significant potential, they should be used judiciously and in workflows that minimize their limitations.