From Literature to Lab: Hardware-Independent Autonomous Chemical Synthesis with Reinforcement Learning

Chemical synthesis, a fundamental process in chemical engineering, traditionally requires extensive manual intervention and expertise, particularly in interpreting scientific literature and translating it into executable workflows. Autonomous systems offer the potential to revolutionize this field by enabling robots to autonomously read scientific literature and form general synthesis workflows. However, a key challenge lies in integrating diverse hardware components, requiring adaptable architectures that can seamlessly operate across various platforms. Furthermore, these systems must be robust, allowing for real-time error correction, and accessible to non-programmers. This paper presents Autonomous Chemical Synthesis System (ACSS), an adaptable architecture for chemical execution systems designed to address these challenges. ACSS enables robots to autonomously read scientific literature and form general synthesis workflows. We achieve hardware independence through protocol-integration reinforcement learning, enabling seamless integration across platforms. Chemical code and hardware descriptions are compiled and converted into robotic instructions for execution. Natural language error correction allows non-programmers to easily adjust the system. Our key contributions are: (1) Literature to Lab: directly extracting synthesis protocols from scientific articles; (2) Hardware Independence: developing an adaptive, reinforcement learning-based model for diverse hardware setups. ACSS has successfully synthesized 12 diverse compounds from literature, including lidocaine (a painkiller), Dess-Martin periodinane (an oxidizing agent), and alkyl fluoride (a fluorinating agent). This demonstration highlights the potential of NLP and reinforcement learning for efficient and accessible chemical research and production. Click here to find out more on YouTube.