Bridging Innovation and Efficiency: The Promises and Challenges of Self-Driving Labs as Sustainable Drivers for Chemistry

Abstract

Self-driving laboratories (SDLs) are reshaping scientific discovery by combining robotics, artificial intelligence (AI), and data science to automate the full Design-Make-Test-Analyze (DMTA) cycle. This review highlights how SDLs address the inefficiencies of traditional trial-and-error methods through intelligent, autonomous experimentation. We explore key advances in AI, automation, and data infrastructure, as well as the remaining technical challenges. Applications across organic synthesis, materials science, and biotechnology (e.g. such as catalytic reaction optimization, solid-state synthesis, and protein engineering) demonstrate their transformative potential. A recurring theme is the role of SDLs in promoting sustainability by miniaturizing reactions and maximizing sample efficiency through AI and machine learning. Finally, we discuss the requirements for broader adoption, including robust hardware, interoperable software, and high-quality datasets, positioning SDLs as essential tools for next-generation sustainable research.