Autonomous millimeter scale high throughput battery research system (Auto-MISCHBARES) Fuzhan Rahmanian 1,2 , Stefan Fuchs 1,2 , Bojing Zhang 1,2 , Maximilian Fichner 1,3 , Helge Sören Stein 1,2 1 Helmholtz Institute Ulm, Applied Electrochemistry, Germany, 2 Karlsruhe Institute of Technology, Institute of Physical Chemistry, Germany, 3 Karlsruhe Institute of Technology (KIT), Institute of Nanotechnology, Germany The discovery of novel electrolyte-electrode combinations for lithium-ion batteries requires a comprehensive electrochemical characterization that correlates spectroscopy and performance. Herein, we present our autonomous millimeter scale high-throughput battery research system (MISCHBARES) operated by hierarchical autonomous laboratory automation and orchestration (HELAO) framework, integrating modular research instrumentation and AI control. This talk will demonstrate the cathode-electrolyte interphase (CEI) formation in Lithium-ion batteries at various potentials during Li striping/plating in an autonomous setup. For every experiment, we developed an automatic quality control system that is integrated into both the hardware and software to ensure maximum reliability through a fidelity assessment of each individual experiment. We believe complex data analysis and quality control to be the missing puzzle piece towards more complex workflow automation. This is achieved through our Modular and Autonomous Data Analysis Platform (MADAP) and validity tests in our presented MISCHBARES platform, which is able to perform fully automated analysis of various voltammetry and impedance measurements in real-time. Integration of MISCHBARES and MADAP through HELAO and database management system (DMS) enables versatile and complex active learning workflows. We demonstrate the integrated workflow allows for efficient and safe striping/plating protocols, and is suitable for use in a variety of research settings. References 1. Rahmanian, F., Flowers, J., Guevarra, D., Richter, M., Fichtner, M., Donnely, P., ... & Stein, H. S. (2022). Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration.Advanced Materials Interfaces,9(8), 2101987. 2. Allan, D., Caswell, T., Campbell, S., & Rakitin, M. (2019). Bluesky's ahead: A multi-facility collaboration for an a la carte software project for data acquisition and management.Synchrotron Radiation News,32(3), 19-22. 3. Roch, L. M., Häse, F., Kreisbeck, C., Tamayo-Mendoza, T., Yunker, L. P., Hein, J. E., & Aspuru-Guzik, A. (2020). ChemOS: An orchestration software to democratize autonomous discovery.PLoS One,15(4), e0229862. 4. Häse, F., Aldeghi, M., Hickman, R. J., Roch, L. M., & Aspuru-Guzik, A. (2021). Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge.Applied Physics Reviews,8(3), 031406. 5. DeCost, B., Joress, H., Sarker, S., Mehta, A., & Hattrick-Simpers, J. (2022). Towards automated design of corrosion resistant alloy coatings with an autonomous scanning droplet cell.arXiv preprint arXiv:2203.17049. 6. Seifrid, M., Pollice, R., Aguilar-Granda, A., Morgan Chan, Z., Hotta, K., Ser, C. T., ... & Aspuru-Guzik, A. (2022). Autonomous chemical experiments: challenges and perspectives on establishing a self-driving lab.Accounts of Chemical Research,55(17), 2454-2466. 7. Pendleton, I. M., Cattabriga, G., Li, Z., Najeeb, M. A., Friedler, S. A., Norquist, A. J., ... & Schrier, J. (2019). Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management.MRS Communications,9(3), 846-859
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