  Discovering new materials can have significant scientific and technological
implications but remains a challenging problem today due to the enormity of the
chemical space. Recent advances in machine learning have enabled data-driven
methods to rapidly screen or generate promising materials, but these methods
still depend heavily on very large quantities of training data and often lack
the flexibility and chemical understanding often desired in materials
discovery. We introduce LLMatDesign, a novel language-based framework for
interpretable materials design powered by large language models (LLMs).
LLMatDesign utilizes LLM agents to translate human instructions, apply
modifications to materials, and evaluate outcomes using provided tools. By
incorporating self-reflection on its previous decisions, LLMatDesign adapts
rapidly to new tasks and conditions in a zero-shot manner. A systematic
evaluation of LLMatDesign on several materials design tasks, in silico,
validates LLMatDesign's effectiveness in developing new materials with
user-defined target properties in the small data regime. Our framework
demonstrates the remarkable potential of autonomous LLM-guided materials
discovery in the computational setting and towards self-driving laboratories in
the future.
