Catalysts of the energy transition

Catalysts of the energy transition

The Max-Planck-Cardiff-Center Funcat lays the foundations for the systematic development of chemical reaction accelerators

When it comes to the energy transition, there has to be the right chemistry. This makes it possible to store electricity from the wind and sun in fuels and base materials for chemical production and also to use CO2 for this purpose. However, the corresponding chemical compounds can only be produced efficiently with the right catalysts; these are, however currently still in short supply. In the now officially opened Max-Planck-Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (Funcat), three Max Planck Institutes and the Cardiff University have joined forces to pursue new paths in catalyst research which rely, among other things, on artificial intelligence. As a test case for the new approach, the researchers are developing, among other things, reaction accelerators that convert CO2 into useful substances.

Catalysts are an economic superpower: reaction accelerators reduce the energy required for many chemical reactions, direct them specifically to desired products, and even make some conversions possible in the first place. It’s no surprise, then, that they are involved in the production of 85 percent of all industrial products and contribute an estimated one-quarter of global economic output. And they are likely to become even more significant if fossil raw materials are increasingly replaced in the future, electricity from renewable sources requires storage, and vehicles such as aircraft and ships will burn synthetic fuels. 

Big data and artificial intelligence

The Max Planck Institutes für Kohlenforschung, for Chemical Energy Conversion and the Fritz Haber Institute as well as the Cardiff Catalysis Institute are involved in Funcat. Their partnership aims to pursue innovative approaches to rational catalyst design in catalysis research. This means that instead of developing new reaction accelerators by trial and error, as has mostly been done so far, calculations will predict the catalytic behavior of materials and thus significantly reduce the experimental effort required in their development.

For the systematic search for suitable candidates, the researchers are using the Nomad database, in which measurement and computational results on the catalytic properties of numerous materials are stored on the initiative of Matthias Scheffler, director emeritus at the Fritz-Haber- Institute, and artificial intelligence. It can use the data from metals, alloys and metal oxides that have already been studied to infer how well new materials are suited to a desired reaction. It also takes into account the fact that a catalyst changes as it works, and allows predictions to be made about how these changes can be controlled to help rather than hinder its work.

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