  Transformer-based large language models are rapidly advancing in the field of
machine learning research, with applications spanning natural language,
biology, chemistry, and computer programming. Extreme scaling and reinforcement
learning from human feedback have significantly improved the quality of
generated text, enabling these models to perform various tasks and reason about
their choices. In this paper, we present an Intelligent Agent system that
combines multiple large language models for autonomous design, planning, and
execution of scientific experiments. We showcase the Agent's scientific
research capabilities with three distinct examples, with the most complex being
the successful performance of catalyzed cross-coupling reactions. Finally, we
discuss the safety implications of such systems and propose measures to prevent
their misuse.

Chemical Physics;Computation and Language