Frontier Superconducting Materials Group

Recent advances in materials informatics (MI) have highlighted the urgent need for experimental platforms capable of translating machine-learning-derived candidate materials into reproducible solid-state syntheses. To address this demand, a generic experimental automation framework was established by integrating heterogeneous laboratory instruments as nodes within the Robot Operating System 2 (ROS2), including low-level I/O-controlled devices via programmable logic controllers (PLCs). The system supports both parallel and sequential process execution while combining Python-based high-level control with C++-based timing-critical operations to ensure high reproducibility. The framework was implemented for arc melting of solid intermetallic bulk materials, where repeated consecutive syntheses were completed within a few hours, yielding phase-pure samples with highly reproducible physical properties. The developed automation system has been released as open-source software on GitHub.

Experimental automation plays an increasingly important role in modern materials research, enabling higher efficiency, reproducibility, and systematic exploration of complex synthesis processes. As materials systems and experimental workflows become more sophisticated, manual operation alone is no longer sufficient to ensure consistent and scalable experimentation.

Our research is currently focusing on establishing a flexible and extensible automation framework for materials synthesis, providing a general experimental infrastructure that supports reliable and repeatable materials development across diverse experimental settings.