The design of novel materials plays a key role in the advancement of technologies in any application field. It is therefore crucial that the materials research is pursued with optimal effectiveness and efficiency. Modern computational materials design in synergy with concepts from big data processing and -storage can largely contribute to meet this requirement. For example, the systematic investigation of a large set of bulk materials can be realized fast and cost effective with high-throughput (HT) electronic structure methods.
The general procedure for a HT-approach is to compute the properties of interest of a large set of possible materials. The information is then ideally stored in a searchable database. The last step is the materials search and selection. With statistical and graphical means, the properties of a large set of materials can be visualized. In fig. 1, the formation energy of a binary alloy (FePt) is shown as an example.
Fig. 1: The alloy formation energy of Fe-Pt alloys as function of the composition. Many different structures have been screened (red crosses) and the most stable structures lay on the blue line (convex hull). Data taken from the AFLOWLIB repository.
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To compare and predict the performance of heterogeneous catalysts, it is important to have a standard way to describe the catalytic activity. The catalytic activity describes how good a catalyst is working for a given reaction. When we have a normalized measure for the catalytic activity, we can compare different catalysts and find out which is the best one. But this means, that we have to do a lot of experiments and try and fail until we found a good catalyst. It would be better, if we could predict whether a material will be a good catalysts for a given reaction or not.
The catalytic activity is dependent on the physical and chemical properties of the catalyst material, and of course the reaction conditions, i.e. temperature, pressure and the reactant concentrations. So, if we want to predict the catalytic activity of a material, we have to have information about the material's properties. As we will see in the following, a few parameters can be sufficient to get an idea on the performance of a material as a catalyst for a given reaction. To predict the catalyst quality we can use the Sabatier principle.
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