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

TL;DR

NIMS-OS addresses the lack of a generic, modular control software to couple AI-driven decision making with robotic experiments for automated materials exploration. It introduces a Python-based orchestration platform where AI modules (PHYSBO, BLOX, PDC, RE) and robotic modules (STAN, NAREE) operate on a common candidates dataset to realize a closed loop. The system is demonstrated on autonomous electrolyte exploration, achieving 384 experiments across 10 hours without human intervention and identifying high-performing electrolyte compositions, underscoring the platform's potential to standardize and accelerate materials discovery. By providing standardized interfaces, visualization tools, and a GUI, NIMS-OS offers a scalable foundation for future incorporation of additional AI algorithms and robotic systems, advancing digital transformation in materials science.

Abstract

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, 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.

Figures (5)

• Figure 1: Image of the combinations of AI algorithms and robotic systems via NIMS-OS.
• Figure 2: Procedures in NIMS-OS and roles of each Python scripts.
• Figure 3: (Top panels) Examples of the candidates files of the initial stage and that after some experiments. Here, an example for the case that $N=9$ is shown. (Bottom panels) Examples for the list of descriptors depending on the types of search space. If the continuous parameter space is considered, $D = \{ \mathbf{x}_i \}_{i=1,...,N}$ is the discretized parameters. When the combination of materials is the search space, the bit strings where the material used is represented by 1 and the material not used is represented by 0 is $D = \{ \mathbf{x}_i \}_{i=1,...,N}$. Furthermore, materials descriptors from compositions obtained by such as magpieong_python_2013ward_general-purpose_2016 and fingerprint of molecules obtained by such as RDKitnoauthor_rdkit_nodate would be used as $D = \{ \mathbf{x}_i \}_{i=1,...,N}$.
• Figure 4: Operation screen of the NIMS-OS GUI version.
• Figure 5: Output results from NIMS-OS for automated exploration for electrolytes using NAREE system: (a) history_step.png and (b) history_best.png by nimsos.visualization.plot_history and (c) distribution.png by nimsos.visualization.plot_distribution.plot. The target property is the discharge time and its unit is sec. In the first cycle, RE is used to generate initial states. After the second cycle, PHYSBO is used.