  Large language models (LLMs) have emerged as powerful tools in chemistry,
significantly impacting molecule design, property prediction, and synthesis
optimization. This review highlights LLM capabilities in these domains and
their potential to accelerate scientific discovery through automation. We also
review LLM-based autonomous agents: LLMs with a broader set of tools to
interact with their surrounding environment. These agents perform diverse tasks
such as paper scraping, interfacing with automated laboratories, and synthesis
planning. As agents are an emerging topic, we extend the scope of our review of
agents beyond chemistry and discuss across any scientific domains. This review
covers the recent history, current capabilities, and design of LLMs and
autonomous agents, addressing specific challenges, opportunities, and future
directions in chemistry. Key challenges include data quality and integration,
model interpretability, and the need for standard benchmarks, while future
directions point towards more sophisticated multi-modal agents and enhanced
collaboration between agents and experimental methods. Due to the quick pace of
this field, a repository has been built to keep track of the latest studies:
https://github.com/ur-whitelab/LLMs-in-science.
