用大模型自动验证化学文献合成流程，实现从论文到机器实验的全流程自动化。
Validation of the Scientific Literature via Chemputation Augmented by Large Language Models
- 通过大模型解析文献并转为通用化学代码，驱动机器人执行
- 在真实机器人系统上成功复现4个文献中的合成实验
- 安全可验证，适合需要高重复性与自动化合成的研究者
Chemputation 是使用通用符号语言编程化学机器人进行实验的过程，但文献常因模糊表述导致错误且难读。尽管标准化合成数据报告已有进展，自动复现已发表合成仍需大量人工。本文提出一种基于大语言模型（LLM）的化学研究代理工作流，实现合成文献的自动验证。该流程可自主提取合成步骤与分析数据，将其转换为通用的 XDL 代码，在特定硬件环境中模拟执行，并最终在 XDL 控制的机器人系统上实际运行。此方法展示了基于 LLM 的工作流在化学机器人自主合成中的潜力。由于 XDL 的抽象性，该方法具备安全性、可验证性和可扩展性，幻觉内容无法被直接执行，且代码可加密。与以往仅覆盖部分流程、依赖固定规则或缺乏物理验证的工作不同，本方法实现了四个真实文献合成案例的直接执行。我们预计该工作流将显著提升机器人驱动合成研究的自动化水平，优化数据提取，增强可重复性、可扩展性与安全性。
原文摘要 · Abstract (English)
Chemputation is the process of programming chemical robots to do experiments using a universal symbolic language, but the literature can be error prone and hard to read due to ambiguities. Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains, including natural language processing, robotic control, and more recently, chemistry. Despite significant advancements in standardizing the reporting and collection of synthetic chemistry data, the automatic reproduction of reported syntheses remains a labour-intensive task. In this work, we introduce an LLM-based chemical research agent workflow designed for the automatic validation of synthetic literature procedures. Our workflow can autonomously extract synthetic procedures and analytical data from extensive documents, translate these procedures into universal XDL code, simulate the execution of the procedure in a hardware-specific setup, and ultimately execute the procedure on an XDL-controlled robotic system for synthetic chemistry. This demonstrates the potential of LLM-based workflows for autonomous chemical synthesis with Chemputers. Due to the abstraction of XDL this approach is safe, secure, and scalable since hallucinations will not be chemputable and the XDL can be both verified and encrypted. Unlike previous efforts, which either addressed only a limited portion of the workflow, relied on inflexible hard-coded rules, or lacked validation in physical systems, our approach provides four realistic examples of syntheses directly executed from synthetic literature. We anticipate that our workflow will significantly enhance automation in robotically driven synthetic chemistry research, streamline data extraction, improve the reproducibility, scalability, and safety of synthetic and experimental chemistry.
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