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.

Our objectives

A modelling and simulation tool for the computational screening of organic chemicals based on their potential performance in energy storage systems.
Data management systems to standardize and store the data generated for further use in model validation and self-optimization procedures.
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 platforms and for modelling and simulation tools, improving their accuracy.

Our PhD positions

Electrochemical high-throughput analytics and electrolyte production
Automated potentio-dynamic electrochemical characterisation
Data-driven and semantic framework for redox flow battery modelling, characterisation and design optimisation
Stability of flow battery electrolytes
Electron transfer kinetics on functionalised electrodes
ML-based methods to describe the potential energy surfaces for organic electrodes and electrolytes
Improving the accuracy of solubility and solution-free energy predictions
Reactor modelling for redox shuttle concepts
High-throughput synthesis of redox shuttles
Machine Learning-based inverse design of synthesis protocols
Machine learning multi-objective optimisation of electrolytes
Model predictive control for flow batteries
Synthesis of new active materials for flow batteries
High-throughput cell characterisation and system performance prediction
A unified framework for end-to-end automated optimisation of flow batteries
Electrochemical double layer and meso-scale modelling
Cell-scale modelling for high-throughput screening

Funded by the European Union (Grant Agreement no. 101168943).
Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union.
Neither the European Union nor the granting authority can be held responsible for them.

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