Doctoral network for high-throughput screening, synthesis and characterization of active materials for flow batteries
"With the many possibilities we have for obtaining superior performance in redox flow batteries, I am glad to be involved in PREDICTOR's project of optimizing these possibilities using machine learning. I am sure that we can advance RFB technology significantly with the collaborative multi-scale research enabled by PREDICTOR "
About my thesis -
Machine learning multi-objective optimisation of electrolytes
My project focuses on developing data fusion algorithms to integrate information from multiple experimental electrochemical characterization techniques (e.g. conductivity cells, UV/Vis spectroscopy, Cyclic Voltammetry) and the electrode processing models that I will develop.
With the fused data, electrode formulations will be optimized with regard to multiple target electrochemical properties, using Machine Learning algorithms. The project also involves performing numerical simulations to describe the electrode manufacturing process