Data‐driven Design of Enhanced In‐based Catalyst for CO2 to Methanol Reaction

The environmental impact of unsustainable CO2 emissions calls for immediate action. One of the main methods for large‐scale reduction of CO2 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, In2O3‐based catalysts have been discussed as target materials for conversion of CO2 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 CO2 to methanol. The method shows the impact, as results show improved performance for selected candidates, compared to the reference In2O3‐system. This confirms the potential of the selected descriptors as criteria for a new way of targeted design of alternative catalysts. An accelerated data‐driven catalyst design strategy is proposed, which exploits the synergy between high‐throughput experimentation and computation, to detect candidate material. Obtained results demonstrate notable improvements in the performance of the selected candidates compared to the reference In2O3‐system.