Publication date: 22nd July 2026
Self-driving laboratories (SDLs) represent a transformative paradigm in chemical and materials discovery, integrating automated hardware with artificial intelligence to drastically accelerate research cycles. Realizing their full potential, however, requires moving away from fragile, laboratory-specific scripts toward a unified and robust digital infrastructure.
This presentation first outlines the fundamental architecture of automated laboratories, examining core design principles for constructing scalable, multi-disciplinary infrastructure. Building upon these principles, a minimal, language-agnostic orchestration foundation is introduced. This framework structures laboratory behavior into a four-level hierarchy: primitives, unit operations, state-preserving unit flows, and workflows. By establishing a rigorous typing discipline and state-preservation contracts, the architecture enables safe parallel execution and robust validation. Crucially, this layered abstraction democratizes workflow design, allowing complex experimental tasks to be efficiently distributed across different scientists, software engineers, and domain experts under a unified "Lab as Code" paradigm.
Finally, the discussion explores how databases and structured knowledge extracted from scientific literature can be seamlessly integrated into these automated ecosystems. By bridging the gap between historical literature data and autonomous execution, this approach provides a scalable foundation for continuous, data-driven scientific discovery.
All authors acknowledge that this research was partially supported by funding provided to the University of Toronto’s Acceleration Consortium through the Canada First Research Excellence Fund (CFREF2022-00042). S.P.-G. acknowledges financial support from the Comunidad de Madrid through the César Nombela talent attraction program (grant no. 2025-T1/COM 36440).
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