A new project will replace the slow, trial-and-error hunt for battery materials with an automated system that designs, makes, and tests candidate chemicals in a single, self-improving loop. Today, developing better materials for energy storage—such as the organic chemicals used in flow batteries—relies on researchers manually synthesising and testing one compound at a time. This is slow and inefficient. PREDICTOR aims to speed this up dramatically by combining computational screening, automated chemical synthesis, and AI-driven optimisation. The system will predict which organic molecules are worth making, synthesise them automatically, test their performance, and feed the results back to improve both the experiments and the computer models. If it works, the method could cut years off the development cycle for new battery electrolytes. Flow batteries store renewable energy from wind and solar on the electricity grid, but their widespread use is limited by the cost and performance of available materials. Faster discovery of better active materials would make grid-scale energy storage cheaper and more reliable, helping to stabilise power supplies as the UK shifts away from fossil fuels. The project will validate the system by building and testing three demonstrator flow battery cells.
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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 selfoptimization 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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