Large Language Models for Scientific Discovery: A Survey of Methods, Applications, and Future Directions

• LLMs significantly accelerate scientific discovery by automating literature review, hypothesis generation, and experimental design. • Three main paradigms exist: knowledge extraction, hypothesis generation, and experimental planning, each with distinct strengths and limitations. • Challenges such as data quality, interpretability, and reproducibility must be addressed to ensure reliable and ethical use of LLMs in science. • Future directions include multimodal LLMs, active learning, and human-in-the-loop systems to enhance scientific reasoning and discovery.