Validation of the Scientific Literature via Chemputation Augmented by Large Language Models

TL;DR

This paper presents an LLM-based workflow that automates the validation, translation, simulation, and physical execution of synthetic chemistry procedures from literature, enhancing reproducibility and scalability in chemical research.

Contribution

It introduces a novel autonomous workflow integrating LLMs and XDL code for end-to-end validation and execution of synthetic procedures on robotic systems.

Findings

Successfully executed four syntheses from literature using the workflow.

Demonstrated safe, secure, and scalable automation with XDL abstraction.

Enhanced reproducibility and validation of chemical procedures.

Abstract

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.