Chemical synthesis, which is crucial for advancing material synthesis and
drug discovery, impacts various sectors including environmental science and
healthcare. The rise of technology in chemistry has generated extensive
chemical data, challenging researchers to discern patterns and refine synthesis
processes. Artificial intelligence (AI) helps by analyzing data to optimize
synthesis and increase yields. However, AI faces challenges in processing
literature data due to the unstructured format and diverse writing style of
chemical literature. To overcome these difficulties, we introduce an end-to-end
AI agent framework capable of high-fidelity extraction from extensive chemical
literature. This AI agent employs large language models (LLMs) for prompt
generation and iterative optimization. It functions as a chemistry assistant,
automating data collection and analysis, thereby saving manpower and enhancing
performance. Our framework's efficacy is evaluated using accuracy, recall, and
F1 score of reaction condition data, and we compared our method with human
experts in terms of content correctness and time efficiency. The proposed
approach marks a significant advancement in automating chemical literature
extraction and demonstrates the potential for AI to revolutionize data
management and utilization in chemistry.

通过大型语言模型技术，我们提出了一种全面的人工智能代理框架，能够高度准确地从大量化学文献中提取信息，实现化学文献的自动化处理，从而节省人力资源并提升性能。这一方法在化学文献处理方面具有重要的实践价值，并展示了人工智能在化学数据管理和利用方面的潜力。