Descriptor-based approaches are among the most promising candidates for future materials design. Today databases for various descriptors exist and identifying a proper one often depends on chemical intuition. In catalysis such approaches have been successfully applied for homogeneous systems and surface alloys. However, due to their complex nature similar general approaches for heterogeneous single-site catalysts have not been proposed so far. In this work we present a descriptor-based approach to describe the adsorption of CO and NO to Fe, Co, Ni and Cu sites in the zeolites SSZ-13 and mordenite (MOR). Based upon the zeolite genome (i.e. the distribution of Al sites inside the material) we propose a set of 7 different active sites in SSZ-13 and 15 active sites in MOR. We identify a set of descriptors based upon electronic structure, stability of active sites and charge to create a database for such sites. In a second step we use a combination of a machine learning approach and Taguchi methods to identify the most important descriptors for our problem. This allows to arrive at an R2 value of over 0.95 in describing the adsorption of CO and NO. To the best of our knowledge this is the first general descriptor-based approach to predict molecular adsorption on single-site catalysts. Based upon Brønsted-Evans-Polanyi relationships this fully machine-learning based approach should also be able to predict reaction barriers for selected reactions, a key feature for the design of such catalysts.

Developing a predictive, descriptor based approach for CO and NO adsorption to Fe, Co, Ni and Cu sites in zeolites

249th National Meeting (2015)

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