Autonomous millimeter scale high throughput battery research system (Auto-MISCHBARES)

11 January 2024, Version 3
This content is a preprint and has not undergone peer review at the time of posting.


Discoveries of novel electrolyte-electrode combinations require comprehensive structure-property-interface correlations. Herein, we present an autonomous millimeter scale high-throughput battery research system (MISCHBARES) operated by hierarchical autonomous laboratory automation and orchestration (HELAO) which integrates modular research instrumentation and AI control. This paper will present cathode electrolyte interphase (CEI) formation in lithium-ion batteries at various potentials by correlating high-throughput electrochemistry and spectroscopy. We believe quality control and complex data analysis to be the missing puzzle piece towards more complex workflow automation. Auto-MISCHBARES integrates automatic quality control for both hardware and software to ensure high reliability through an on-the-fly fidelity assessment of each individual experiment. Data analysis is achieved through our Modular and Autonomous Data Analysis Platform (MADAP) in our presented platform, which is capable of performing a fully automated analysis of various voltammetry measurements in real-time. Integration of MISCHBARES and MADAP through HELAO enables versatile and complex active learning workflows in the discovery of new materials. We demonstrate this integrated workflow for reliable charging/discharging protocols.


Data Analysis
Web Server
Web Communication
Machine Learning
Data Management

Supplementary materials

Mischbares - Supplementary information
Additional material supporting this study details about: - Schematic of user interface - Elemental distribution of screen printed electrodes - Electrode production - MADAP analysis example - Remaining XPS Spectra

Supplementary weblinks


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