Data‐driven Design of Enhanced In‐based Catalyst for CO 2 to Methanol Reaction

The environmental impact of unsustainable CO 2 emissions calls for immediate action. One of the main methods for large‐scale reduction of CO 2 emissions is conversion of carbon dioxide to valuable feedstocks like energy carriers or chemicals. Realization of this goal requires catalysts showing high‐performance characteristics under the relevant industrial conditions. In recent years, In 2 O 3 ‐based catalysts have been discussed as target materials for conversion of CO 2 to methanol in the presence of hydrogen. Optimization of this catalytic system via conventional routes requires massive resources in terms of extensive testing, synthesis, and characterization. In our study, we take an alternative approach and exploit high‐throughput computation to create a database for oxygen vacancy formation energy and explore a large number of candidates, which motivates the selection of a subset of materials for the synthesis of bulk and supported catalysts, to be tested in hydrogenation of CO 2 to methanol. The method shows the impact, as results show improved performance for selected candidates, compared to the reference In 2 O 3 ‐system. This confirms the potential of the selected descriptors as criteria for a new way of targeted design of alternative catalysts.