Rethinking chemical research in the age of large language models.

Abstract

Large language models (LLMs) offer opportunities for advancing chemical research, including planning, optimization, data analysis, automation and knowledge management. Deploying LLMs in active environments, where they interact with tools and data, can greatly enhance their capabilities. However, challenges remain in evaluating their performance and addressing ethical issues such as reproducibility, data privacy and bias. Here we discuss ongoing and potential integrations of LLMs in chemical research, highlighting existing challenges to guide the effective use of LLMs as active scientific partners.