ChemOS 2.0: an orchestration architecture for chemical self-driving laboratories

Abstract

Self-driving laboratories (SDLs), which combine automated experimental hardware with computational experiment planning, have emerged as powerful tools for accelerating materials discovery. The intrinsic complexity created by their multitude of components requires an effective orchestration platform to ensure the correct operation of diverse experimental setups. Existing orchestration frameworks, however, are either tailored to specific setups or have not been implemented for real-world synthesis. To address these issues, we introduce ChemOS 2.0, an orchestration architecture that efficiently coordinates communication, data exchange, and instruction management among modular laboratory components. By treating the laboratory as an "operating system" ChemOS 2.0 combines ab-initio calculations, experimental orchestration and statistical algorithms to guide closed-loop operations. To demonstrate its capabilities, we showcase ChemOS 2.0 in a case study focused on discovering organic laser molecules. The results confirm the ChemOS 2.0's prowess in accelerating materials research and demonstrate its potential as a valuable design for future SDL platforms.