The discovery of new energy materials is becoming a large-scale challenge that is far beyond the reach of experimentation but also stretching the limits of conventional computation. At DIFFER; our AMD research group is working on to improve the speed and the prediction capability of computational methods for the discovery of new energy materials. We use machine learning (ML), which is essentially a method to make predictions and to optimize a performance criterion based on the available example data.
Please note: unless otherwise specified, the internships are only available for students with a nationality of an EU-member state and/or students from a Dutch university.
The computational MSc thesis project is part of a theory-experiment collaboration effort between DIFFER and our industrial partner. The overall aim is to understand the fundamentals of new Fe-based model catalysts, and to tune them further for the Fischer-Tropsch (FT) synthesis of fuels using renewable energy. The fundamental aim is to know how Fe metal layers grow on different Cu metal substrates and how these newly grown Fe layers behave during the adsorption of atomic and molecular species, such as H, C, O, and CO, which are all needed for the synthesis of commercially valuable fuels.
Photo-electrochemical (PEC) solar fuel conversion is one of the most promising techniques to convert solar energy directly into its most versatile form of energy, a fuel. However, the efficiency is still low and degradation too high. We have several open BSc/MSc/internship projects for both experiments and modeling & simulation.