Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent and three organocatalysts and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow’s effectiveness in automating a diverse set of chemical tasks. Our work not only aids expert chemists and lowers barriers for non-experts but also fosters scientific advancement by bridging the gap between experimental and computational chemistry.

大型语言模型 (LLM) 在跨领域的任务中表现出了强大的性能，但在解决化学相关问题方面却遇到了困难。这些模型还缺乏对外部知识源的访问，限制了它们在科学应用中的有用性。我们推出 ChemCrow，这是一种法学硕士化学试剂，旨在完成有机合成、药物发现和材料设计方面的任务。通过集成 18 个专家设计的工具并使用 GPT-4 作为法学硕士，ChemCrow 增强了法学硕士在化学方面的表现，并出现了新的功能。我们的代理人自主计划并执行了一种驱虫剂和三种有机催化剂的合成，并指导了一种新型发色团的发现。我们的评估（包括法学硕士和专家评估）证明了 ChemCrow 在自动化多种化学任务方面的有效性。我们的工作不仅帮助专业化学家并降低非专家的障碍，而且还通过弥合实验化学和计算化学之间的差距来促进科学进步。