Publication date: 15th December 2025
Self-Driving Laboratories (SDLs), which realize autonomous materials exploration through the integration of black-box optimization (BBO) methods (leveraging machine learning/artificial intelligence) and robotic experimental systems, propose and execute experiments under promising, yet unexplored, conditions without human intervention.
To easily facilitate this integration, we developed NIMO, an open-source middleware. NIMO treats each experimental system and BBO method as interchangeable modules, enabling the flexible realization of diverse SDLs through arbitrary combinations. NIMO standardly implements multiple advanced BBO algorithms, including Bayesian optimization (BO) variants, phase diagram construction, and objective-free search. Furthermore, its modular design significantly streamlines the implementation of new algorithms, allowing newly developed exploration strategies to be rapidly adopted in robotic experiments.
This talk introduces the NIMO framework, showcases its modular architecture, and presents concrete examples of self-driving laboratories realized using NIMO. We will also demonstrate successful application results in materials exploration, highlighting NIMO's capability to accelerate autonomous discovery. NIMO is available at https://github.com/NIMS-DA/nimo.
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