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.

LLMatDesign 是一个基于大型语言模型的可解释材料设计框架，通过使用 LLM 代理来翻译人类指令、修改材料并使用提供的工具评估结果，以零样本的方式适应新任务和条件，通过在几个材料设计任务上系统评估证实其在小数据领域中开发具有用户定义目标属性的新材料的有效性，展示了在计算环境中基于自主 LLM 引导的材料发现对于未来的自动化实验室的显著潜力。