Prof. Dr. Johannes Margraf from the University of Bayreuth and his team have developed a promising method to improve the efficiency of electrocatalysts. Using simulations and artificial intelligence, the researchers have developed a computer program that can simultaneously optimize several properties of the catalyst. Fuel cells are one of several important key technologies in the energy transition. However, the dependence on rare metals such as platinum as catalysts is a major obstacle to their widespread use. Prof. Dr. Johannes Margraf's research addresses this challenge by incorporating material costs into the optimization process from the outset. This innovation could help to develop cost-effective alternatives to platinum as a catalyst material in fuel cells.
High entropy alloys (HEAs) are a promising type of material for electrocatalysis, a process in which they help accelerate chemical reactions that take place in batteries or fuel cells. Unlike conventional metal catalysts, these materials consist of a mixture of many elements. As a result, they have a very complex structure and could therefore have better catalytic properties in electrolyzers and fuel cells. However, it is difficult to find the best mixture of elements for a specific application. By developing an algorithm that uses simulations and artificial intelligence to simultaneously improve several properties of the catalyst, such as activity, cost and stability, the researchers from Bayreuth and the Fritz Haber Institute in Berlin were able to predict many new HEAs. The algorithm was specifically tested for oxygen reduction in fuel cells, where expensive platinum is normally used as a catalyst. Catalysts were found that are just as active as platinum, but cost much less - only 10 percent compared to platinum. In addition, catalysts were identified that are two and a half times as active as platinum, but at a similar cost. The Bayreuth researcher's theoretical predictions have yet to be confirmed by practical experiments.
