Data-centric heterogeneous catalysis: identifying rules and materials genes of alkane selective oxidation

Artificial intelligence (AI) can accelerate materials design by identifying the key parameters correlated with the performance. However, widely used AI methods require big data, and only the smallest part of the available data in heterogeneous catalysis meets the quality requirement for data-efficient AI. Here, we use rigorous experimental procedures, designed to consistently take into account the kinetics of the catalyst active states formation, in order to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts towards ethane, propane, and n-butane oxidation. These catalyst materials are based on vanadium or manganese redox-active elements (RAEs) and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator (SISSO) approach to the consistent data set, we identify nonlinear property-function relationships depending on several key parameters, reflecting the intricate interplay of underlying processes governing selective oxidation. This approach indicates the most relevant characterization techniques and shows how the catalyst properties may be tuned in order to achieve the desired performance. For example, to achieve high olefin yields, the catalyst must have a high specific surface area, a low concentration of surface RAE, and the ability to change the surface RAE oxidation states under reaction conditions with respect to vacuum. These parameters are measured by N2 adsorption, x-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. They reflect the relevance of local transport, site isolation, surface redox activity, and the materials dynamical restructuring under reaction conditions. Although the relationship describing the even more challenging oxygenate yields shares similarities with that for olefin yields, a parameter reflecting the importance of specific surface sites, derived from the analysis of the carbon 1s XPS spectra, is additionally identified as key for high selectivity to oxygenates.