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 ChemOS 2.0's prowess in accelerating materials research and demonstrate its potential as a valuable design for future SDL platforms. 

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

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NIMS-OS (NIMS Orchestration System) is a Python library created to realize a closed loop of robotic experiments and artificial intelligence (AI) without human intervention for automated materials exploration. It uses various combinations of modules to operate autonomously. Each module acts as an AI for materials exploration or a controller for a robotic experiments. As AI techniques, optimization tools for PHYSics based on Bayesian Optimization (PHYSBO), BoundLess Objective-free eXploration (BLOX), Phase Diagram Construction (PDC), and Random Exploration (RE) methods can be used. Moreover, a system called NIMS automated robotic electrochemical experiments (NAREE) is available as a set of robotic experimental equipment. Visualization tools for the results are also included, which allows users to check the optimization results in real time. Newly created modules for AI and robotic experiments can be added easily to extend the functionality of the system. In addition, we developed a GUI application to control NIMS-OS. To demonstrate the operation of NIMS-OS, we consider an automated exploration for new electrolytes. NIMS-OS is available at https://github.com/nimsos-dev/nimsos. 

https://www.tandfonline.com/doi/pdf/10.1080/27660400.2023.2232297?needAccess=true&role=button

NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science

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NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science