PREDICTOR

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.

2024

2028