Abstract:

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