Fraunhofer Institute for Chemical Technology ICT
Fraunhofer Institute for Chemical Technology ICT

Research project PREDICTOR

Doctoral network for high-throughput screening, synthesis and characterization of active materials for flow batteries

PREDICTOR aims to establish a rapid, high-throughput method to identify and develop materials for electrochemical energy storage.

Logo Predictor

PREDICTOR is a research and training project funded by the European Union’s Marie-Sklodowska-Curie programme. It involves 7 partners and 15 associated partners from 11 different countries, who will recruit 17 PhD students for the project.

PREDICTOR aims to establish a rapid, high-throughput method to identify and develop materials for electrochemical energy storage. It will enable the rapid identification, synthesis and characterization of materials within a coherent development chain, replacing conventional trial-and-error developments. 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.

PREDICTOR will focus on the high-throughput development of materials for redox flow batteries (RFBs), as one of the most promising technologies for medium- to long-term energy storage. In RFBs, a reversible chemical change occurring within liquid electrolytes enables the rapid storage and release of energy: the accelerated development of organic electrolytes thus offers significant potential to improve these systems and tailor them for specific applications. However, the new methods will be transferable to all electrochemical energy storage techniques.

The project work is divided into three development areas:

  • Modelling, simulation and computational high-throughput screening
  • Experimental high-throughput methods
  • Data management and validation, experimental demonstration

Training

PREDICTOR will equip 17 doctoral candidates with the scientific and transferable skills needed for careers in electrochemical energy storage.

They will gain in-depth knowledge in modeling, highthroughput experimentation, self-optimization, and data management across scales. Entrepreneurship training and industrial secondments will bridge theory and practice, providing real-world experience and diverse perspectives. 

Further information on PREDICTOR

Background information

Funding: HORIZON, MSCA-DN

Funding code: 101168943

Duration: September 2024 to August 2028

Website: www.rfb-predictor.eu

LinkedIn: @predictor-doctoral-network-for-high-throughput-methods-for-flow-battery-material-development

Project Partners

Associated Partners

HTE GmbH, Harvard University, University of Bayreuth, Golin Wissenschaftsmanagement, RWTH Aachen, University of Amsterdam, University of Leiden, University of Gent, Université de Picardie Jules Verne