The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.

Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descriptive parameters correlated with the underlying processes triggering, favoring, or hindering the performance. In analogy to genes in biology, these parameters might be called “materials genes” of heterogeneous catalysis. However, widely used AI methods require big data, and only the smallest part of the available data 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, to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts toward ethane, propane, and n-butane oxidation reactions. These materials are based on vanadium or manganese redox-active elements and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator symbolic-regression approach to the consistent data set, we identify nonlinear property–function relationships depending on several key parameters and reflecting the intricate interplay of processes that govern the formation of olefins and oxygenates: local transport, site isolation, surface redox activity, adsorption, and the material dynamical restructuring under reaction conditions. These processes are captured by parameters derived from N2 adsorption, X-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. The data-centric approach indicates the most relevant characterization techniques to be used for catalyst design and provides “rules” on how the catalyst properties may be tuned in order to achieve the desired performance.

Atomic layer deposition (ALD) of POx on V2O5 powder was applied as a tool to tailor active and selective sites of a bulk catalyst. ALD leads to homogeneous P deposition on the V2O5 surface with linear increase of P content with each ALD cycle. The catalyst performance was evaluated and correlated to structural motifs identified by detailed characterization methods. The catalytic conversion of butane to maleic anhydride (MAN) was chosen as proof‐of‐concept reaction. The selectivity towards MAN increases with ALD cycle number from 1–3 ALD cycles and remains constant at higher ALD cycles. Restructuring of the catalyst surface is induced by steam during reaction conditions at elevated temperatures. Excessive P is migrating away from the catalyst surface to form various VOPO4 polymorphs revealing partially but homogeneously covered V2O5 by P. The formed VOPO4 species barely contribute to the yield to MAN. Solid‐state 31P‐NMR was used to identify fingerprints relevant for selectivity and activity. This work shows that synthesizing model catalysts by atomic layer deposition combined with detailed analytics can reveal property‐performance relationships.

A holistic understanding of the key catalytic features of vanadyl­(IV) pyrophosphate enabling high maleic anhydride (MAN) yields in n-butane oxidation has fostered a debate which has continued since the finding of the catalyst. Under reaction conditions, vanadium­(V) orthophosphate structure fragments were detected on the surface of the catalyst. However, single-phase αII- and β-VVOPO4 reveal a much lower catalytic performance. This study shows that introducing Nb into αII-VOPO4 forming a solid solution (V1-x Nb x )OPO4 yields a bulk material with tunable catalytic properties. Selectivities of S MAN = 48% at a conversion of X n‑butane = 30% on (V0.1Nb0.9)­OPO4 are shown to be related to the isolation of surface V-sites, which surpass known VOPO4 catalysts by far. A boost in the overall n-butane consumption and MAN selectivity under alkane-rich feed conditions is shown to be another characteristic of (V1-x Nb x )­OPO4, leading to a highly increased MAN productivity. XPS studies reveal that a progressive replacement of V by Nb induces a reduction of the averaged oxidation state of near-surface V from +4.7 to +4.3, a finding that correlates linearly with an elevated MAN selectivity. This study experimentally confirms site isolation and electronic environment of the near-surface V-species as the key catalytic properties, from which catalyst design rules are derived to optimize partial oxidation reactions.

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