PREDICTOR High-throughput screening, synthesis and characterization of active materials for flow batteries

Summary

PREDICTOR aims to establish a rapid, high-throughput method to identify and develop materials for electrochemical energy storage. This method will comprise:
• A modelling and simulation tool for the computational screening of organic chemicals based on their potential performance in energy storage systems.
• Automated chemical synthesis, electrolyte production and characterization methods, so that the chemicals identified in the screening step can be rapidly produced and tested for their suitability in energy storage applications.
• Artificial-intelligence-based self-optimization methods that allow experimental data from material characterization to be fed back into automated experimental methods to enable self-driving laboratory laboratory platforms and for modelling and simulation tools, improving their accuracy.
• Data management systems to standardize and store the data generated for further use in model validation and self-optimization procedures
This approach will allow the rapid identification, synthesis and characterization of materials within a coherent development chain, replacing conventional trial-and-error developments. It will exploit the synergies between several emerging markets (digital technologies, artificial intelligence, high-throughput experimentation, renewable energy storage), providing the recruited doctoral candidates (DCs) with a valuable interdisciplinary skill set. To validate the PREDICTOR system, the case study will be active materials and electrolytes for redox-flow batteries. Within the project, three demonstrator battery cells (TRL3-4) will be assembled and tested with the newly developed materials.

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