EUROPEAN CONGRESS AND EXHIBITION ON ADVANCED MATERIALS AND PROCESSES - EUROMAT 2021

The generalized-gradient approximation within DFT is a workhorse of computational chemistry especially in modeling metal particles and chemical transformations at their surface. Mixed metal oxides that may be reduced during operation pose a very special challenge to computational methods, as standard DFT methods do no longer provide reliable results. This talk will highlight typical challenges arising when modeling such reducible mixed metal oxides and how to keep track of the arising oxidation states.
For the mixed metal oxide catalysts, we discuss elementary building principles to understand the experimental structure. We will highlight the immense variability introduced by variable oxidation states and flexible occupancies, resulting in a multitude of structures, currently defying a comprehensive modeling in the traditional way. Therefore, we resort to identifying guiding principles for understanding these materials that are then refined by machine learning methods on the example of MoVO type oxides. This enables us to characterize the most stable material variants without time consuming DFT modeling. Finally, we apply this knowledge to the adsorption of small molecules at the surface of mixed metal oxides, providing a glance on the chemistry to be expected.