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

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 skillset. 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.