Toward an Autonomous Robotic Battery Materials Research Platform Powered by Automated Workflow and Ontologized Findable, Accessible, Interoperable, and Reusable Data Management

The discovery of novel battery materials has been accelerated by advanced modeling and machine learning. However, their integration into battery cells remains constrained by the necessity for experimental validation. The status of development and validation of the automated robotic battery materials research platform Aurora is presented, enabling rapid testing of scientific hypotheses and validation of physical models. Aurora integrates electrolyte formulation, battery cell assembly, and battery cell cycling into a stepwise automated application‐relevant workflow. The different features of the Aurora platform can be leveraged to design experiments elucidating the impact of cycling parameters, electrode composition, and balancing, and electrolyte formulation on battery performance and long‐term cycling stability with the example of NMC||graphite and LFP||graphite cells with carbonate‐based electrolytes, which serve as benchmark battery cell chemistries. A large, structured, dataset with ontologized metadata detailing cell assembly and cycling protocols, alongside corresponding time series cycling data for all cells is provided as open research data. This study establishes Aurora as a powerful research platform for accelerating battery materials research.