Material acceleration platforms can be developed by integrating four key elements from multiple hubs; The DeviceHub consists of hardware components responsible for moving and measuring. ServerHub ensures robust operations and communication workflow through orchestration and API design; The DataHub manages the database environment, and is a key for data integrity and accessibility; Lastly, the MultiAnalysisHub provides a wide spectrum of analytics, from statistical assessments to computer vision methods . The integration of all the Hubs is the precursor of acceleration in material development and therefore, by following this design philosophy, our Auto-MISCHBARES platform is developed to perform fully autonomous electrochemical measurements and is tailored to study the formation of SEI and CEI. In addition, it can monitor oxidation state changes in active materials. . The interface (Supp. 1) allows researchers to formulate a series of experimental electrochemical protocols, which are then dispatched to a central orchestrator via an Application Programming Interface (API). The orchestrator, serving as a control system, manages the scheduling, execution, and monitoring of the experimental array by delivering actions to the individual Hubs, which are the granular unit of activity and can execute actions tasks such as positioning, measurement, real-time analytical processing and provides the basis for live visualization. Upon the completion of individual experiments or a sequence thereof, the orchestrator compiles and dispatches a comprehensive analytical report to the experimentalist. The DeviceHub features a high-precision robotic manipulator adept at positioning the Scanning Droplet Cell (SDC) head accurately onto the substrate, where a Hamilton Microlab 600 precision syringe system c delivers electrolyte directly when contact is established. The Autolab Potentiostat b is employed to perform electrochemical measurements once the system is primed for analysis. Quality control protocols, including drop and contact detection and chatbot-based communication, facilitate automation and oversight of the experimental processes. Furthermore, experiments proceed in three automated stages: waste disposal, cleaning, and electrochemical measurement, each executed according to predefined specifications and with real-time plotting provided by a Bokeh visualizer for immediate "at-a-glance" feedback. Post-measurement, the data undergoes immediate analysis via MADAP, which calculates electrochemical markers and generates corresponding plots for in-depth analysis. This processed data is then systematically cataloged in a Data Management System (DMS), ensuring data provenance, facile retrieval, and compartmentalization in alignment with the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. It is possible to optimize the subsequent experiment with active learning (AL) algorithms through the HELAO platform.